From g.lemaitre58 at gmail.com  Fri May  1 03:58:11 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Fri, 1 May 2020 09:58:11 +0200
Subject: [scikit-learn] Why does sklearn require one-hot-encoding for
 categorical features? Can we have a "factor" data type?
In-Reply-To: <CAE2FW2kotVbpP+0yDhEG79xJ=5o-Ez0h1oudhM3dxWU6r7cytw@mail.gmail.com>
References: <CAE2FW2mpiuU8mgFFeoU3oyC4pjBHR_tLAK=sLw=qrJ6VcDi_Rg@mail.gmail.com>
 <20200430201206.75tl2ohkxo5yerlo@phare.normalesup.org>
 <SN2PR0801MB222364E7F43972D8EC201C88A0AA0@SN2PR0801MB2223.namprd08.prod.outlook.com>
 <CAE2FW2kotVbpP+0yDhEG79xJ=5o-Ez0h1oudhM3dxWU6r7cytw@mail.gmail.com>
Message-ID: <CACDxx9gzcYa9t5V1y_4My03VT62bXM6SgbGsiFdNBZXBO0Sttg@mail.gmail.com>

OrdinalEncoder is the equivalent of pd.factorize and will work in the
scikit-learn ecosystem.

However, be aware that you should not just swap OneHotEncoder to
OrdinalEncoder just at your wish.
It depends of your machine learning pipeline.

As mentioned by Gael, tree-based algorithm will be fine with
OrdinalEncoder. If you have a linear model,
then you need to use the OneHotEncoder if the categories do not have any
order.

I will just refer to one notebook that we taught in EuroScipy last year:
https://github.com/lesteve/euroscipy-2019-scikit-learn-tutorial/blob/master/rendered_notebooks/02_basic_preprocessing.ipynb

On Fri, 1 May 2020 at 05:11, C W <tmrsg11 at gmail.com> wrote:

> Hermes,
>
> That's an interesting function. Does it work with sklearn after
> factorize?  Is there any example? Thanks!
>
> On Thu, Apr 30, 2020 at 6:51 PM Hermes Morales <paisanohermes at hotmail.com>
> wrote:
>
>> Perhaps pd.factorize could hello?
>>
>> Obtener Outlook para Android <https://aka.ms/ghei36>
>>
>> ------------------------------
>> *From:* scikit-learn <scikit-learn-bounces+paisanohermes=
>> hotmail.com at python.org> on behalf of Gael Varoquaux <
>> gael.varoquaux at normalesup.org>
>> *Sent:* Thursday, April 30, 2020 5:12:06 PM
>> *To:* Scikit-learn mailing list <scikit-learn at python.org>
>> *Subject:* Re: [scikit-learn] Why does sklearn require one-hot-encoding
>> for categorical features? Can we have a "factor" data type?
>>
>> On Thu, Apr 30, 2020 at 03:55:00PM -0400, C W wrote:
>> > I've used R and Stata software, none needs such transformation. They
>> have a
>> > data type called "factors", which is different from "numeric".
>>
>> > My problem with OHE:
>> > One-hot-encoding results in large number of features. This really blows
>> up
>> > quickly. And I have to fight curse of dimensionality with PCA
>> reduction. That's
>> > not cool!
>>
>> Most statistical models still not one-hot encoding behind the hood. So, R
>> and stata do it too.
>>
>> Typically, tree-based models can be adapted to work directly on
>> categorical data. Ours don't. It's work in progress.
>>
>> G
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>>
>> https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmail.python.org%2Fmailman%2Flistinfo%2Fscikit-learn&amp;data=02%7C01%7C%7Ce7aa6f99b7914a1f84b208d7ed430801%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637238744453345410&amp;sdata=e3BfHB4v5VFteeZ0Zh3FJ9Wcz9KmkUwur5i8Reue3mc%3D&amp;reserved=0
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200501/c5f58c55/attachment-0001.html>

From tmrsg11 at gmail.com  Fri May  1 11:53:45 2020
From: tmrsg11 at gmail.com (C W)
Date: Fri, 1 May 2020 11:53:45 -0400
Subject: [scikit-learn] Why does sklearn require one-hot-encoding for
 categorical features? Can we have a "factor" data type?
In-Reply-To: <CACDxx9gzcYa9t5V1y_4My03VT62bXM6SgbGsiFdNBZXBO0Sttg@mail.gmail.com>
References: <CAE2FW2mpiuU8mgFFeoU3oyC4pjBHR_tLAK=sLw=qrJ6VcDi_Rg@mail.gmail.com>
 <20200430201206.75tl2ohkxo5yerlo@phare.normalesup.org>
 <SN2PR0801MB222364E7F43972D8EC201C88A0AA0@SN2PR0801MB2223.namprd08.prod.outlook.com>
 <CAE2FW2kotVbpP+0yDhEG79xJ=5o-Ez0h1oudhM3dxWU6r7cytw@mail.gmail.com>
 <CACDxx9gzcYa9t5V1y_4My03VT62bXM6SgbGsiFdNBZXBO0Sttg@mail.gmail.com>
Message-ID: <CAE2FW2m+osx=mWT-1JjRwStPw4__Oh4F3F_H6622+KnVRG48Ug@mail.gmail.com>

Thank you for the link, Guilaumme. In my particular case, I am working on
random forest classification.

The notebook seems great. I will have to go through it in detail. I'm still
fairly new at using sklearn.

Thank you for everyone's quick response, always feeling loved on here! :)



On Fri, May 1, 2020 at 4:00 AM Guillaume Lema?tre <g.lemaitre58 at gmail.com>
wrote:

> OrdinalEncoder is the equivalent of pd.factorize and will work in the
> scikit-learn ecosystem.
>
> However, be aware that you should not just swap OneHotEncoder to
> OrdinalEncoder just at your wish.
> It depends of your machine learning pipeline.
>
> As mentioned by Gael, tree-based algorithm will be fine with
> OrdinalEncoder. If you have a linear model,
> then you need to use the OneHotEncoder if the categories do not have any
> order.
>
> I will just refer to one notebook that we taught in EuroScipy last year:
>
> https://github.com/lesteve/euroscipy-2019-scikit-learn-tutorial/blob/master/rendered_notebooks/02_basic_preprocessing.ipynb
>
> On Fri, 1 May 2020 at 05:11, C W <tmrsg11 at gmail.com> wrote:
>
>> Hermes,
>>
>> That's an interesting function. Does it work with sklearn after
>> factorize?  Is there any example? Thanks!
>>
>> On Thu, Apr 30, 2020 at 6:51 PM Hermes Morales <paisanohermes at hotmail.com>
>> wrote:
>>
>>> Perhaps pd.factorize could hello?
>>>
>>> Obtener Outlook para Android <https://aka.ms/ghei36>
>>>
>>> ------------------------------
>>> *From:* scikit-learn <scikit-learn-bounces+paisanohermes=
>>> hotmail.com at python.org> on behalf of Gael Varoquaux <
>>> gael.varoquaux at normalesup.org>
>>> *Sent:* Thursday, April 30, 2020 5:12:06 PM
>>> *To:* Scikit-learn mailing list <scikit-learn at python.org>
>>> *Subject:* Re: [scikit-learn] Why does sklearn require one-hot-encoding
>>> for categorical features? Can we have a "factor" data type?
>>>
>>> On Thu, Apr 30, 2020 at 03:55:00PM -0400, C W wrote:
>>> > I've used R and Stata software, none needs such transformation. They
>>> have a
>>> > data type called "factors", which is different from "numeric".
>>>
>>> > My problem with OHE:
>>> > One-hot-encoding results in large number of features. This really
>>> blows up
>>> > quickly. And I have to fight curse of dimensionality with PCA
>>> reduction. That's
>>> > not cool!
>>>
>>> Most statistical models still not one-hot encoding behind the hood. So, R
>>> and stata do it too.
>>>
>>> Typically, tree-based models can be adapted to work directly on
>>> categorical data. Ours don't. It's work in progress.
>>>
>>> G
>>> _______________________________________________
>>> scikit-learn mailing list
>>> scikit-learn at python.org
>>>
>>> https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmail.python.org%2Fmailman%2Flistinfo%2Fscikit-learn&amp;data=02%7C01%7C%7Ce7aa6f99b7914a1f84b208d7ed430801%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637238744453345410&amp;sdata=e3BfHB4v5VFteeZ0Zh3FJ9Wcz9KmkUwur5i8Reue3mc%3D&amp;reserved=0
>>> _______________________________________________
>>> scikit-learn mailing list
>>> scikit-learn at python.org
>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>
>
> --
> Guillaume Lemaitre
> Scikit-learn @ Inria Foundation
> https://glemaitre.github.io/
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200501/86e25f6c/attachment.html>

From jonpsy101 at gmail.com  Sat May  2 00:57:00 2020
From: jonpsy101 at gmail.com (sai_ng)
Date: Sat, 2 May 2020 10:27:00 +0530
Subject: [scikit-learn] Random Binning Features
Message-ID: <CAP+w2ZifQeQMDN0bmFSReFwkvgp-+vk9qo6D=1_XgqP6yczTgg@mail.gmail.com>

Hey folks !
Hope you're all doing well.

I'm developing Random Fourier Feature implementation in c++ for a
repository. Scikits implementation on RBFSampler has been really helpful,
and I must say that I'm charmed but how compact, yet powerful each line of
code is.

I'm writing this mail because I couldn't find your implementation of Random
Binning Features, is it under development?. I tried searching in the issues
but, to no avail. I noticed you've put few of your algorithms on a
different repository for ex:
https://scikit-learn-extra.readthedocs.io/en/latest/generated/sklearn_extra.kernel_approximation.Fastfood.html.


Overall, I'd like to know if it's under development or has there been any
draft/proposal or is it already implemented. I'd greatly appreciate if you
could point me to other sources (if not here) which have successfully
implemented it in code (preferably python/c++)

"Hit me back,
Just to chat,
Your biggest fan,
This is stan"
~ Eminem: Stan
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200502/90aafdff/attachment.html>

From g.lemaitre58 at gmail.com  Sat May  2 04:13:30 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Sat, 2 May 2020 10:13:30 +0200
Subject: [scikit-learn] Vote: Add Adrin Jalali to the scikit-learn
 technical committee
In-Reply-To: <CALQtMBZk-cz7LJaNoHns5LKpUW35oS=ULXXFW+JNP8KhjWGvzA@mail.gmail.com>
References: <7d9ffac3-35d0-7e30-9c96-3c125b4f9fe7@gmail.com>
 <8f0f6d85-2d52-4bb5-bd5d-c13d60377364@Canary>
 <CADeotZpBLJAJ0-=wv0AQY83BGUCLoefdkgJjw=baXQQ-TvOdaw@mail.gmail.com>
 <CAGKmC=u2C4-NW-yF5T6KC3xBv36KzGfTsdm-h5bq2_aRMvgTKQ@mail.gmail.com>
 <CAAkaFLUTdi3kK-KFLzRhB0vdvEJShOtFvQZJGXs5=mL9oxYLXA@mail.gmail.com>
 <CALQtMBZk-cz7LJaNoHns5LKpUW35oS=ULXXFW+JNP8KhjWGvzA@mail.gmail.com>
Message-ID: <CACDxx9heHRP3xLR8fzU4oUP55HQQgVRO3nfx1DXP2cE+CK_6oQ@mail.gmail.com>

+1

On Tue, 28 Apr 2020 at 20:59, Joris Van den Bossche <
jorisvandenbossche at gmail.com> wrote:

> +1
>
> On Tue, 28 Apr 2020 at 01:34, Joel Nothman <joel.nothman at gmail.com> wrote:
>
>> +1
>>
>> On Tue, 28 Apr 2020 at 02:23, Tom DLT <tom.duprelatour at orange.fr> wrote:
>>
>>> +1
>>>
>>> Le lun. 27 avr. 2020, ? 07 h 00, Alexandre Gramfort <
>>> alexandre.gramfort at inria.fr> a ?crit :
>>>
>>>> +1
>>>> _______________________________________________
>>>> scikit-learn mailing list
>>>> scikit-learn at python.org
>>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>>
>>> _______________________________________________
>>> scikit-learn mailing list
>>> scikit-learn at python.org
>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200502/35ff0b66/attachment.html>

From ahowe42 at gmail.com  Tue May  5 08:29:18 2020
From: ahowe42 at gmail.com (Andrew Howe)
Date: Tue, 5 May 2020 13:29:18 +0100
Subject: [scikit-learn] Fwd: StackingClassifier
In-Reply-To: <CANnYi3T=L4VdNrHU4Pd1Otx3dS2h94Nm3Yer0L2qs1kGWB+czA@mail.gmail.com>
References: <CANnYi3T=L4VdNrHU4Pd1Otx3dS2h94Nm3Yer0L2qs1kGWB+czA@mail.gmail.com>
Message-ID: <CANnYi3RVJHvmGJvQpix3ybLR7cq0arTSUDqu-Sd1JF+LTFiLyQ@mail.gmail.com>

Hi All - gentle nudge in case anybody has an idea about this.

Andrew

<~~~~~~~~~~~~~~~~~~~~~~~~~~~>
J. Andrew Howe, PhD
LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
Open Researcher and Contributor ID (ORCID)
<http://orcid.org/0000-0002-3553-1990>
Github Profile <http://github.com/ahowe42>
Personal Website <http://www.andrewhowe.com>
I live to learn, so I can learn to live. - me
<~~~~~~~~~~~~~~~~~~~~~~~~~~~>


---------- Forwarded message ---------
From: Andrew Howe <ahowe42 at gmail.com>
Date: Thu, Apr 30, 2020 at 6:05 PM
Subject: StackingClassifier
To: Scikit-learn user and developer mailing list <scikit-learn at python.org>


Hi All

Quick question about the stacking classifier
<https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html>.
How do I know the order of the features that the final estimator uses? I've
got an example which I've created like this (the LGRG and KSVM objects were
previously defined, but as they seem they would be):

passThrough = True
finalEstim = DecisionTreeClassifier(random_state=42)
stkClas = StackingClassifier(estimators=[('Logistic Regression', LGRG),
('Kernel SVM', KSVM)],
                             cv=crossValInput, passthrough=passThrough,
final_estimator=finalEstim,
                             n_jobs=-1)

Given this setup, I *think* the features input to the final estimator are

   - Logistic regression prediction probabilities for all classes
   - Kernel SVM prediction probabilities for all classes
   - original features of data passed into the stacking classifier

I can find no documentation on this, though, and don't know of any relevant
attribute on the final estimator. I need this to help interpret the final
estimator tree - and specifically to provide feature labels for plot_tree.

Thanks!
Andrew

<~~~~~~~~~~~~~~~~~~~~~~~~~~~>
J. Andrew Howe, PhD
LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
Open Researcher and Contributor ID (ORCID)
<http://orcid.org/0000-0002-3553-1990>
Github Profile <http://github.com/ahowe42>
Personal Website <http://www.andrewhowe.com>
I live to learn, so I can learn to live. - me
<~~~~~~~~~~~~~~~~~~~~~~~~~~~>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200505/b4adfa2c/attachment.html>

From g.lemaitre58 at gmail.com  Tue May  5 08:40:28 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Tue, 5 May 2020 14:40:28 +0200
Subject: [scikit-learn] Fwd: StackingClassifier
In-Reply-To: <CANnYi3RVJHvmGJvQpix3ybLR7cq0arTSUDqu-Sd1JF+LTFiLyQ@mail.gmail.com>
References: <CANnYi3T=L4VdNrHU4Pd1Otx3dS2h94Nm3Yer0L2qs1kGWB+czA@mail.gmail.com>
 <CANnYi3RVJHvmGJvQpix3ybLR7cq0arTSUDqu-Sd1JF+LTFiLyQ@mail.gmail.com>
Message-ID: <CACDxx9hvSoMPrUcogom76b_XabLsSLb5++4k674b0DayzuDjgQ@mail.gmail.com>

Your analysis is correct:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py#L59

It will be the prediction of each learner in the order in the list given
and finally the features which are pass-through.

It would nice when we will be able to propagate feature names :)

On Tue, 5 May 2020 at 14:31, Andrew Howe <ahowe42 at gmail.com> wrote:

> Hi All - gentle nudge in case anybody has an idea about this.
>
> Andrew
>
> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
> J. Andrew Howe, PhD
> LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
> Open Researcher and Contributor ID (ORCID)
> <http://orcid.org/0000-0002-3553-1990>
> Github Profile <http://github.com/ahowe42>
> Personal Website <http://www.andrewhowe.com>
> I live to learn, so I can learn to live. - me
> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>
>
> ---------- Forwarded message ---------
> From: Andrew Howe <ahowe42 at gmail.com>
> Date: Thu, Apr 30, 2020 at 6:05 PM
> Subject: StackingClassifier
> To: Scikit-learn user and developer mailing list <scikit-learn at python.org>
>
>
> Hi All
>
> Quick question about the stacking classifier
> <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html>.
> How do I know the order of the features that the final estimator uses? I've
> got an example which I've created like this (the LGRG and KSVM objects were
> previously defined, but as they seem they would be):
>
> passThrough = True
> finalEstim = DecisionTreeClassifier(random_state=42)
> stkClas = StackingClassifier(estimators=[('Logistic Regression', LGRG),
> ('Kernel SVM', KSVM)],
>                              cv=crossValInput, passthrough=passThrough,
> final_estimator=finalEstim,
>                              n_jobs=-1)
>
> Given this setup, I *think* the features input to the final estimator are
>
>    - Logistic regression prediction probabilities for all classes
>    - Kernel SVM prediction probabilities for all classes
>    - original features of data passed into the stacking classifier
>
> I can find no documentation on this, though, and don't know of any
> relevant attribute on the final estimator. I need this to help interpret
> the final estimator tree - and specifically to provide feature labels for
> plot_tree.
>
> Thanks!
> Andrew
>
> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
> J. Andrew Howe, PhD
> LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
> Open Researcher and Contributor ID (ORCID)
> <http://orcid.org/0000-0002-3553-1990>
> Github Profile <http://github.com/ahowe42>
> Personal Website <http://www.andrewhowe.com>
> I live to learn, so I can learn to live. - me
> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200505/95ad6782/attachment.html>

From ahowe42 at gmail.com  Tue May  5 08:47:55 2020
From: ahowe42 at gmail.com (Andrew Howe)
Date: Tue, 5 May 2020 13:47:55 +0100
Subject: [scikit-learn] Fwd: StackingClassifier
In-Reply-To: <CACDxx9hvSoMPrUcogom76b_XabLsSLb5++4k674b0DayzuDjgQ@mail.gmail.com>
References: <CANnYi3T=L4VdNrHU4Pd1Otx3dS2h94Nm3Yer0L2qs1kGWB+czA@mail.gmail.com>
 <CANnYi3RVJHvmGJvQpix3ybLR7cq0arTSUDqu-Sd1JF+LTFiLyQ@mail.gmail.com>
 <CACDxx9hvSoMPrUcogom76b_XabLsSLb5++4k674b0DayzuDjgQ@mail.gmail.com>
Message-ID: <CANnYi3QOq=yvLUT4ZoDrU85Zop6zvawEexNXROTx0rRJ6V9ZcA@mail.gmail.com>

Great - thanks! Yes, it would be very nice to have feature names
automatically propagate throughout sklearn.

Andrew

<~~~~~~~~~~~~~~~~~~~~~~~~~~~>
J. Andrew Howe, PhD
LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
Open Researcher and Contributor ID (ORCID)
<http://orcid.org/0000-0002-3553-1990>
Github Profile <http://github.com/ahowe42>
Personal Website <http://www.andrewhowe.com>
I live to learn, so I can learn to live. - me
<~~~~~~~~~~~~~~~~~~~~~~~~~~~>


On Tue, May 5, 2020 at 1:42 PM Guillaume Lema?tre <g.lemaitre58 at gmail.com>
wrote:

> Your analysis is correct:
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py#L59
>
> It will be the prediction of each learner in the order in the list given
> and finally the features which are pass-through.
>
> It would nice when we will be able to propagate feature names :)
>
> On Tue, 5 May 2020 at 14:31, Andrew Howe <ahowe42 at gmail.com> wrote:
>
>> Hi All - gentle nudge in case anybody has an idea about this.
>>
>> Andrew
>>
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>> J. Andrew Howe, PhD
>> LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
>> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
>> Open Researcher and Contributor ID (ORCID)
>> <http://orcid.org/0000-0002-3553-1990>
>> Github Profile <http://github.com/ahowe42>
>> Personal Website <http://www.andrewhowe.com>
>> I live to learn, so I can learn to live. - me
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>>
>>
>> ---------- Forwarded message ---------
>> From: Andrew Howe <ahowe42 at gmail.com>
>> Date: Thu, Apr 30, 2020 at 6:05 PM
>> Subject: StackingClassifier
>> To: Scikit-learn user and developer mailing list <scikit-learn at python.org
>> >
>>
>>
>> Hi All
>>
>> Quick question about the stacking classifier
>> <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html>.
>> How do I know the order of the features that the final estimator uses? I've
>> got an example which I've created like this (the LGRG and KSVM objects were
>> previously defined, but as they seem they would be):
>>
>> passThrough = True
>> finalEstim = DecisionTreeClassifier(random_state=42)
>> stkClas = StackingClassifier(estimators=[('Logistic Regression', LGRG),
>> ('Kernel SVM', KSVM)],
>>                              cv=crossValInput, passthrough=passThrough,
>> final_estimator=finalEstim,
>>                              n_jobs=-1)
>>
>> Given this setup, I *think* the features input to the final estimator are
>>
>>    - Logistic regression prediction probabilities for all classes
>>    - Kernel SVM prediction probabilities for all classes
>>    - original features of data passed into the stacking classifier
>>
>> I can find no documentation on this, though, and don't know of any
>> relevant attribute on the final estimator. I need this to help interpret
>> the final estimator tree - and specifically to provide feature labels for
>> plot_tree.
>>
>> Thanks!
>> Andrew
>>
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>> J. Andrew Howe, PhD
>> LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
>> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
>> Open Researcher and Contributor ID (ORCID)
>> <http://orcid.org/0000-0002-3553-1990>
>> Github Profile <http://github.com/ahowe42>
>> Personal Website <http://www.andrewhowe.com>
>> I live to learn, so I can learn to live. - me
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>
>
> --
> Guillaume Lemaitre
> Scikit-learn @ Inria Foundation
> https://glemaitre.github.io/
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200505/f5e16e44/attachment-0001.html>

From adrin.jalali at gmail.com  Wed May  6 02:56:52 2020
From: adrin.jalali at gmail.com (Adrin)
Date: Wed, 6 May 2020 08:56:52 +0200
Subject: [scikit-learn] ANN: scikit-learn 0.23 RC1
Message-ID: <CAEOrW4_DtB8R+MGEO9gaOomBm=wdNaOYSZ1VTKApJ0jPLt9g-g@mail.gmail.com>

Thanks to all our 200+ contributors, we are announcing a release candidate
for the upcoming release.

On top of a few exciting features, we're also deprecating positional
arguments in
many places where the constructor/method accepts many arguments.
for example, SVC(.5, "poly") will need to be expressed as SVC(C=.5,
kernel="poly"),
and SVC(C, kernel) as SVC(C=C, kernel=kernel).

Please give it a try and let us know if there are any issues for us to fix
them for the final release.

Release highlights:
https://scikit-learn.org/0.23/auto_examples/release_highlights/plot_release_highlights_0_23_0.html

Changelog: https://scikit-learn.org/0.23/whats_new/v0.23.html#changes-0-23

Happy testing,
Adrin
On behalf of the scikit-learn team
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200506/3d3236d2/attachment.html>

From fernando.wittmann at gmail.com  Wed May  6 09:36:55 2020
From: fernando.wittmann at gmail.com (Fernando Marcos Wittmann)
Date: Wed, 6 May 2020 10:36:55 -0300
Subject: [scikit-learn] Why does sklearn require one-hot-encoding for
 categorical features? Can we have a "factor" data type?
In-Reply-To: <CAE2FW2m+osx=mWT-1JjRwStPw4__Oh4F3F_H6622+KnVRG48Ug@mail.gmail.com>
References: <CAE2FW2mpiuU8mgFFeoU3oyC4pjBHR_tLAK=sLw=qrJ6VcDi_Rg@mail.gmail.com>
 <20200430201206.75tl2ohkxo5yerlo@phare.normalesup.org>
 <SN2PR0801MB222364E7F43972D8EC201C88A0AA0@SN2PR0801MB2223.namprd08.prod.outlook.com>
 <CAE2FW2kotVbpP+0yDhEG79xJ=5o-Ez0h1oudhM3dxWU6r7cytw@mail.gmail.com>
 <CACDxx9gzcYa9t5V1y_4My03VT62bXM6SgbGsiFdNBZXBO0Sttg@mail.gmail.com>
 <CAE2FW2m+osx=mWT-1JjRwStPw4__Oh4F3F_H6622+KnVRG48Ug@mail.gmail.com>
Message-ID: <CABM1w2Sh-Wgp=av3g_3p_MD8hRsTMS6VY=JFE-Kfb6qxVW1+nw@mail.gmail.com>

That's an excellent discussion! I've always wondered how other tools like R
handled naturally categorical variables or not. LightGBM has a scikit-learn
API which handles categorical features by inputting their columns names (or
indexes):
```
import lightgbm
lgb=lightgbm.LGBMClassifier()
lgb.fit(*X*, *y*, *feature_name=... *, *categorical_feature=... *)

```

Where:

- feature_name (list of strings or 'auto', optional (default='auto')) ?
Feature names. If ?auto? and data is pandas DataFrame, data columns names
are used.

- categorical_feature (list of strings or int, or 'auto', optional
(default='auto')) ? Categorical features. If list of int, interpreted as
indices. If list of strings, interpreted as feature names (need to specify
feature_name as well). If ?auto? and data is pandas DataFrame, pandas
unordered categorical columns are used. All values in categorical features
should be less than int32 max value (2147483647).


As a suggestion, Scikit-Learn could add a `categorical_feature` parameter
in the tree-based estimators in order to work on the same way.

On Fri, May 1, 2020 at 12:54 PM C W <tmrsg11 at gmail.com> wrote:

> Thank you for the link, Guilaumme. In my particular case, I am working on
> random forest classification.
>
> The notebook seems great. I will have to go through it in detail. I'm
> still fairly new at using sklearn.
>
> Thank you for everyone's quick response, always feeling loved on here! :)
>
>
>
> On Fri, May 1, 2020 at 4:00 AM Guillaume Lema?tre <g.lemaitre58 at gmail.com>
> wrote:
>
>> OrdinalEncoder is the equivalent of pd.factorize and will work in the
>> scikit-learn ecosystem.
>>
>> However, be aware that you should not just swap OneHotEncoder to
>> OrdinalEncoder just at your wish.
>> It depends of your machine learning pipeline.
>>
>> As mentioned by Gael, tree-based algorithm will be fine with
>> OrdinalEncoder. If you have a linear model,
>> then you need to use the OneHotEncoder if the categories do not have any
>> order.
>>
>> I will just refer to one notebook that we taught in EuroScipy last year:
>>
>> https://github.com/lesteve/euroscipy-2019-scikit-learn-tutorial/blob/master/rendered_notebooks/02_basic_preprocessing.ipynb
>>
>> On Fri, 1 May 2020 at 05:11, C W <tmrsg11 at gmail.com> wrote:
>>
>>> Hermes,
>>>
>>> That's an interesting function. Does it work with sklearn after
>>> factorize?  Is there any example? Thanks!
>>>
>>> On Thu, Apr 30, 2020 at 6:51 PM Hermes Morales <
>>> paisanohermes at hotmail.com> wrote:
>>>
>>>> Perhaps pd.factorize could hello?
>>>>
>>>> Obtener Outlook para Android <https://aka.ms/ghei36>
>>>>
>>>> ------------------------------
>>>> *From:* scikit-learn <scikit-learn-bounces+paisanohermes=
>>>> hotmail.com at python.org> on behalf of Gael Varoquaux <
>>>> gael.varoquaux at normalesup.org>
>>>> *Sent:* Thursday, April 30, 2020 5:12:06 PM
>>>> *To:* Scikit-learn mailing list <scikit-learn at python.org>
>>>> *Subject:* Re: [scikit-learn] Why does sklearn require
>>>> one-hot-encoding for categorical features? Can we have a "factor" data type?
>>>>
>>>> On Thu, Apr 30, 2020 at 03:55:00PM -0400, C W wrote:
>>>> > I've used R and Stata software, none needs such transformation. They
>>>> have a
>>>> > data type called "factors", which is different from "numeric".
>>>>
>>>> > My problem with OHE:
>>>> > One-hot-encoding results in large number of features. This really
>>>> blows up
>>>> > quickly. And I have to fight curse of dimensionality with PCA
>>>> reduction. That's
>>>> > not cool!
>>>>
>>>> Most statistical models still not one-hot encoding behind the hood. So,
>>>> R
>>>> and stata do it too.
>>>>
>>>> Typically, tree-based models can be adapted to work directly on
>>>> categorical data. Ours don't. It's work in progress.
>>>>
>>>> G
>>>> _______________________________________________
>>>> scikit-learn mailing list
>>>> scikit-learn at python.org
>>>>
>>>> https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmail.python.org%2Fmailman%2Flistinfo%2Fscikit-learn&amp;data=02%7C01%7C%7Ce7aa6f99b7914a1f84b208d7ed430801%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637238744453345410&amp;sdata=e3BfHB4v5VFteeZ0Zh3FJ9Wcz9KmkUwur5i8Reue3mc%3D&amp;reserved=0
>>>> _______________________________________________
>>>> scikit-learn mailing list
>>>> scikit-learn at python.org
>>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>>
>>> _______________________________________________
>>> scikit-learn mailing list
>>> scikit-learn at python.org
>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>
>>
>>
>> --
>> Guillaume Lemaitre
>> Scikit-learn @ Inria Foundation
>> https://glemaitre.github.io/
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200506/8b4c5d94/attachment-0001.html>

From joel.nothman at gmail.com  Wed May  6 09:43:41 2020
From: joel.nothman at gmail.com (Joel Nothman)
Date: Wed, 6 May 2020 23:43:41 +1000
Subject: [scikit-learn] Why does sklearn require one-hot-encoding for
 categorical features? Can we have a "factor" data type?
In-Reply-To: <CABM1w2Sh-Wgp=av3g_3p_MD8hRsTMS6VY=JFE-Kfb6qxVW1+nw@mail.gmail.com>
References: <CAE2FW2mpiuU8mgFFeoU3oyC4pjBHR_tLAK=sLw=qrJ6VcDi_Rg@mail.gmail.com>
 <20200430201206.75tl2ohkxo5yerlo@phare.normalesup.org>
 <SN2PR0801MB222364E7F43972D8EC201C88A0AA0@SN2PR0801MB2223.namprd08.prod.outlook.com>
 <CAE2FW2kotVbpP+0yDhEG79xJ=5o-Ez0h1oudhM3dxWU6r7cytw@mail.gmail.com>
 <CACDxx9gzcYa9t5V1y_4My03VT62bXM6SgbGsiFdNBZXBO0Sttg@mail.gmail.com>
 <CAE2FW2m+osx=mWT-1JjRwStPw4__Oh4F3F_H6622+KnVRG48Ug@mail.gmail.com>
 <CABM1w2Sh-Wgp=av3g_3p_MD8hRsTMS6VY=JFE-Kfb6qxVW1+nw@mail.gmail.com>
Message-ID: <CAAkaFLXcffVqKRVWq9xYmvXpyDB3Lw7eXHM_GJ74euRR8GcL2w@mail.gmail.com>

When it comes to trees, the API for handling categoricals is simpler than
the implementation. Traditionally, tree-based models' handling of
categorical variables differs from both ordinal and one-hot encoding, while
both of those will work reasonably well for many problems. We are working
on implementing categorical handling in trees (
https://github.com/scikit-learn/scikit-learn/issues/15550,
https://github.com/scikit-learn/scikit-learn/pull/12866)...
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200506/53934042/attachment.html>

From fernando.wittmann at gmail.com  Fri May  8 17:04:14 2020
From: fernando.wittmann at gmail.com (Fernando Marcos Wittmann)
Date: Fri, 8 May 2020 18:04:14 -0300
Subject: [scikit-learn] Why the default max_samples of Random Forest is
 X.shape[0]?
Message-ID: <CABM1w2T4gGYVL5VL=R2kFvma6NRWZ0oRV50z8Ufdx_pn9dGQKQ@mail.gmail.com>

When reading the documentation of Random Forest, I got the following:
```
max_samples : int or float, default=None If bootstrap is True, the number
of samples to draw from X to train each base estimator. - *If None
(default), then draw `X.shape[0]` samples.* - If int, then draw
`max_samples` samples. - If float, then draw `max_samples * X.shape[0]`
samples. Thus, `max_samples` should be in the interval `(0, 1)`.
```

Why does the whole dataset (i.e. X.shape[0] samples from X) is used to
build each tree? That would be equivalent to bootstrap to be False, right?
Wouldn't it be better practices to use as default 2/3 of the size of the
dataset?
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200508/4ab88b1d/attachment.html>

From mlcnworkshop at gmail.com  Sat May  9 14:41:16 2020
From: mlcnworkshop at gmail.com (MLCN Workshop)
Date: Sat, 9 May 2020 20:41:16 +0200
Subject: [scikit-learn] [CFP] The 3rd International workshop on Machine
 Learning in Clinical Neuroimaging (MLCN 2020)
Message-ID: <CAN9Uh4efZz34SqUqiFoeysUzV-SvKf6vt_4p_y_McXZw+hq5jA@mail.gmail.com>

*Please find below the call for papers for the International Workshop of
Machine Learning in Clinical Neuroimaging (MLCN) on 4 October 2020 at
MICCAI 2020 in Lima, Peru. We welcome contributions on novel machine
learning methods and their applications to clinical neuroimaging data.*

The submission deadline is *30 June 2020*, and all MLCN accepted papers
will be eligible for the best paper award of 500 USD.

For more information, please visit https://mlcnws.com/.

Best wishes,
The MLCN 2020 committee

Christos Davatzikos
Andre Marquand
Jonas Richiardi
Emma Robinson
Ahmed Abdulkadir
Cher Bass
Mohamad Habes
Seyed Mostafa Kia
Jane Maryam Rondina
Chantal Tax
Hongzhi Wang
Thomas Wolfers



International Workshop on Machine Learning in Clinical Neuroimaging

4 October 2020 in Lima, Peru



The International Workshop of Machine Learning in Clinical Neuroimaging (
https://mlcnws.com/), a satellite event of MICCAI
(https://miccai2020.org), calls
for original papers in the field of clinical neuroimaging data analysis
with machine learning. The two tracks of the workshop include
methodological innovations as well as clinical applications. This highly
interdisciplinary topic provides an excellent platform to connect
researchers of varying disciplines and to collectively advance the field in
multiple directions.

For the machine learning track, we seek contributions with substantial
methodological novelty in analyzing high-dimensional, longitudinal, and
heterogeneous neuroimaging data using stable, scalable, and interpretable
machine learning models. Topics of interest include but are not limited to:

   - Spatio-temporal brain data analysis
   - Structural data analysis
   - Graph theory and complex network analysis
   - Longitudinal data analysis
   - Model stability and interpretability
   - Model scalability in large neuroimaging datasets
   - Multi-source data integration and multi-view learning
   - Multi-site data analysis, from preprocessing to modeling
   - Domain adaptation, data harmonization, and transfer learning in
   neuroimaging
   - Unsupervised methods for stratifying brain disorders
   - Deep learning in clinical neuroimaging
   - Model uncertainty in clinical predictions
   - ...

In the clinical neuroimaging track, we seek contributions that explore how
the application of advanced machine learning methods help us to move
towards precision medicine for complex brain disorders. Topics of interest
include but are not limited to:

   - Biomarker discovery
   - Refinement of nosology and diagnostics
   - Biological validation of clinical syndromes
   - Treatment outcome prediction
   - Course prediction
   - Analysis of wearable sensors
   - Neurogenetics and brain imaging genetics
   - Mechanistic modeling
   - Brain aging
   - ...


Submission Process:

The workshop seeks high quality, original, and unpublished work that
addresses one or more challenges described above. Papers should be
submitted electronically in Springer Lecture Notes in Computer Science
(LCNS) style (see
https://www.miccai2020.org/en/PAPER-SUBMISSION-GUIDELINE.html#manuscript-format
for detailed author guidelines) using the CMT system at
https://cmt3.research.microsoft.com/MLCN2020. The page limit is 8-pages (text,
figures, and tables) plus up to 2-pages of references. We review the
submissions in a double-blind process. Please make sure that your
submission is anonymous. Accepted papers will be published in a joint
proceeding with the MICCAI 2020 conference.

Best Paper Award:

This year, all MLCN accepted papers will be eligible for the best paper
award. The recipient of the award will be chosen by the MLCN scientific
committee based on the scientific quality and novelty of contributions. The
winner will be announced at the end of the workshop and will receive 500
USD honorarium.

Important Dates:

   - Paper submission deadline: June 30th, 2020
   - Notification of Acceptance: July 24th, 2020
   - Camera-ready Submission: July 31st, 2020
   - Workshop Date: 4 October 2020
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200509/f73713e4/attachment-0001.html>

From joel.nothman at gmail.com  Sun May 10 08:49:33 2020
From: joel.nothman at gmail.com (Joel Nothman)
Date: Sun, 10 May 2020 22:49:33 +1000
Subject: [scikit-learn] Why the default max_samples of Random Forest is
 X.shape[0]?
In-Reply-To: <CABM1w2T4gGYVL5VL=R2kFvma6NRWZ0oRV50z8Ufdx_pn9dGQKQ@mail.gmail.com>
References: <CABM1w2T4gGYVL5VL=R2kFvma6NRWZ0oRV50z8Ufdx_pn9dGQKQ@mail.gmail.com>
Message-ID: <CAAkaFLUYMKbPo4FhjP-5h3Aj2xO18yZAHj8zd8X8pZQx+=b9Hw@mail.gmail.com>

A bootstrap is very commonly a random draw with replacement of equal size
to the original sample.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200510/db37e634/attachment.html>

From fernando.wittmann at gmail.com  Sun May 10 18:40:40 2020
From: fernando.wittmann at gmail.com (Fernando Marcos Wittmann)
Date: Sun, 10 May 2020 19:40:40 -0300
Subject: [scikit-learn] Why the default max_samples of Random Forest is
 X.shape[0]?
In-Reply-To: <CAAkaFLUYMKbPo4FhjP-5h3Aj2xO18yZAHj8zd8X8pZQx+=b9Hw@mail.gmail.com>
References: <CABM1w2T4gGYVL5VL=R2kFvma6NRWZ0oRV50z8Ufdx_pn9dGQKQ@mail.gmail.com>
 <CAAkaFLUYMKbPo4FhjP-5h3Aj2xO18yZAHj8zd8X8pZQx+=b9Hw@mail.gmail.com>
Message-ID: <CABM1w2R1u8PUAstVR9-q8J6Kw32QgTX6TtUxndM6qZ4oDcmGmQ@mail.gmail.com>

My question is why the full dataset is being used as default when building
each tree. That's not random forest. The main point of RF is to build each
tree with a subsample of the full dataset

On Sun, May 10, 2020, 09:50 Joel Nothman <joel.nothman at gmail.com> wrote:

> A bootstrap is very commonly a random draw with replacement of equal size
> to the original sample.
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200510/8ef9e08e/attachment.html>

From fernando.wittmann at gmail.com  Sun May 10 18:42:41 2020
From: fernando.wittmann at gmail.com (Fernando Marcos Wittmann)
Date: Sun, 10 May 2020 19:42:41 -0300
Subject: [scikit-learn] Why the default max_samples of Random Forest is
 X.shape[0]?
In-Reply-To: <CABM1w2R1u8PUAstVR9-q8J6Kw32QgTX6TtUxndM6qZ4oDcmGmQ@mail.gmail.com>
References: <CABM1w2T4gGYVL5VL=R2kFvma6NRWZ0oRV50z8Ufdx_pn9dGQKQ@mail.gmail.com>
 <CAAkaFLUYMKbPo4FhjP-5h3Aj2xO18yZAHj8zd8X8pZQx+=b9Hw@mail.gmail.com>
 <CABM1w2R1u8PUAstVR9-q8J6Kw32QgTX6TtUxndM6qZ4oDcmGmQ@mail.gmail.com>
Message-ID: <CABM1w2RdMD=uiNuUDAPkHLe0eoJLtC1Mn6DQSr1osfN2oPemSA@mail.gmail.com>

Okay, so it's sampling with replacement with same size of the original
dataset. That mean that some of the samples would be repeated for each tree

On Sun, May 10, 2020, 19:40 Fernando Marcos Wittmann <
fernando.wittmann at gmail.com> wrote:

> My question is why the full dataset is being used as default when building
> each tree. That's not random forest. The main point of RF is to build each
> tree with a subsample of the full dataset
>
> On Sun, May 10, 2020, 09:50 Joel Nothman <joel.nothman at gmail.com> wrote:
>
>> A bootstrap is very commonly a random draw with replacement of equal size
>> to the original sample.
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200510/06c46d51/attachment.html>

From fernando.wittmann at gmail.com  Mon May 11 01:20:07 2020
From: fernando.wittmann at gmail.com (Fernando Marcos Wittmann)
Date: Mon, 11 May 2020 02:20:07 -0300
Subject: [scikit-learn] Why the default max_samples of Random Forest is
 X.shape[0]?
In-Reply-To: <CABM1w2RdMD=uiNuUDAPkHLe0eoJLtC1Mn6DQSr1osfN2oPemSA@mail.gmail.com>
References: <CABM1w2T4gGYVL5VL=R2kFvma6NRWZ0oRV50z8Ufdx_pn9dGQKQ@mail.gmail.com>
 <CAAkaFLUYMKbPo4FhjP-5h3Aj2xO18yZAHj8zd8X8pZQx+=b9Hw@mail.gmail.com>
 <CABM1w2R1u8PUAstVR9-q8J6Kw32QgTX6TtUxndM6qZ4oDcmGmQ@mail.gmail.com>
 <CABM1w2RdMD=uiNuUDAPkHLe0eoJLtC1Mn6DQSr1osfN2oPemSA@mail.gmail.com>
Message-ID: <CABM1w2RuHrcdn3ysY62m9c+JjvK7YUZrW--8QXKd3yFperZHhg@mail.gmail.com>

Ohh, I can see now my mistake after reviewing the concept of bootstrapping
and sampling with replacement. I was assuming that the "replacement" was
made only after finishing each tree (i.e. If I was samping 2/3 of data, the
very same data could be selected again for each tree, but no element would
be repeated in a given tree). My apologies. Everything makes sense again

On Sun, May 10, 2020, 19:42 Fernando Marcos Wittmann <
fernando.wittmann at gmail.com> wrote:

> Okay, so it's sampling with replacement with same size of the original
> dataset. That mean that some of the samples would be repeated for each tree
>
> On Sun, May 10, 2020, 19:40 Fernando Marcos Wittmann <
> fernando.wittmann at gmail.com> wrote:
>
>> My question is why the full dataset is being used as default when
>> building each tree. That's not random forest. The main point of RF is to
>> build each tree with a subsample of the full dataset
>>
>> On Sun, May 10, 2020, 09:50 Joel Nothman <joel.nothman at gmail.com> wrote:
>>
>>> A bootstrap is very commonly a random draw with replacement of equal
>>> size to the original sample.
>>> _______________________________________________
>>> scikit-learn mailing list
>>> scikit-learn at python.org
>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>
>>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200511/4fa6bb22/attachment.html>

From nelle.varoquaux at gmail.com  Wed May 13 03:18:17 2020
From: nelle.varoquaux at gmail.com (Nelle Varoquaux)
Date: Wed, 13 May 2020 09:18:17 +0200
Subject: [scikit-learn] Announcing the 2020 John Hunter Excellence in
 Plotting Contest
Message-ID: <CAE-UAvT_O--DNR-Mx16RDE9f89tvgDdtO=dwB6qte8ze2buq5g@mail.gmail.com>

Dear all,


I apologize for the cross posting. This is a reminder for the John Hunter
Excellence in Plotting Contest.

In memory of John Hunter, we are pleased to announce the John Hunter
Excellence in Plotting Contest for 2020. This open competition aims to
highlight the importance of data visualization to scientific progress and
showcase the capabilities of open source software.

Participants are invited to submit scientific plots to be judged by a
panel. The winning entries will be announced and displayed at SciPy 2020 or
announced in the John Hunter Excellence in Plotting Contest website and
youtube channel.

John Hunter?s family are graciously sponsoring cash prizes for the winners
in the following amounts:


   -

   1st prize: $1000
   -

   2nd prize: $750
   -

   3rd prize: $500



   -

   Entries must be submitted by June 1st to the form at
   https://forms.gle/SrexmkDwiAmDc7ej7
   -

   Winners will be announced at Scipy 2020 in Austin, TX or publicly on the
   John Hunter Excellence in Plotting Contest website and youtube channel
   -

   Participants do not need to attend the Scipy conference.
   -

   Entries may take the definition of ?visualization? rather broadly.
   Entries may be, for example, a traditional printed plot, an interactive
   visualization for the web, a dashboard, or an animation.
   -

   Source code for the plot must be provided, in the form of Python code
   and/or a Jupyter notebook, along with a rendering of the plot in a widely
   used format.  The rendering may be, for example, PDF for print, standalone
   HTML and Javascript for an interactive plot, or MPEG-4 for a video. If the
   original data can not be shared for reasons of size or licensing, "fake"
   data may be substituted, along with an image of the plot using real data.
   -

   Each entry must include a 300-500 word abstract describing the plot and
   its importance for a general scientific audience.
   -

   Entries will be judged on their clarity, innovation and aesthetics, but
   most importantly for their effectiveness in communicating a real-world
   problem. Entrants are encouraged to submit plots that were used during the
   course of research or work, rather than merely being hypothetical.
   -

   SciPy and the John Hunter Excellence in Plotting Contest organizers
   reserves the right to display any and all entries, whether prize-winning or
   not, at the conference, use in any materials or on its website, with
   attribution to the original author(s).
   -

   Past entries can be found at https://jhepc.github.io/
   -

   Questions regarding the contest can be sent to jhepc.organizers at gmail.com



John Hunter Excellence in Plotting Contest Co-Chairs

Madicken Munk

Nelle Varoquaux
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200513/b9cbb9fe/attachment.html>

From adrin.jalali at gmail.com  Wed May 13 09:19:43 2020
From: adrin.jalali at gmail.com (Adrin)
Date: Wed, 13 May 2020 15:19:43 +0200
Subject: [scikit-learn] ANN scikit-learn 0.23.0 release
Message-ID: <CAEOrW49nzVm5h41xwnd74f10eMvSYuB6g1Uh3DwYvzwgX0o03w@mail.gmail.com>

We're happy to announce the 0.23.0 release. You can read
the release highlights under
https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_23_0.html

and the long version of the change log under
https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-0

On top of a few exciting features, we're also deprecating positional
arguments in
many places where the constructor/method accepts many arguments.
for example, SVC(.5, "poly") will need to be expressed as SVC(C=.5,
kernel="poly"),
and SVC(C, kernel) as SVC(C=C, kernel=kernel).

This version supports Python versions 3.6 to 3.8. You can
give it a go using `pip install -U scikit-learn` or alternatively
`conda install -c conda-forge scikit-learn` which should be there
soon.

Regards,
Adrin, on behalf of the scikit-learn maintainer team.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200513/652f73c4/attachment-0001.html>

From olivier.grisel at ensta.org  Wed May 13 11:22:05 2020
From: olivier.grisel at ensta.org (Olivier Grisel)
Date: Wed, 13 May 2020 17:22:05 +0200
Subject: [scikit-learn] ANN scikit-learn 0.23.0 release
In-Reply-To: <CAEOrW49nzVm5h41xwnd74f10eMvSYuB6g1Uh3DwYvzwgX0o03w@mail.gmail.com>
References: <CAEOrW49nzVm5h41xwnd74f10eMvSYuB6g1Uh3DwYvzwgX0o03w@mail.gmail.com>
Message-ID: <CAFvE7K52tt8T5BpaecwQs8gG54-=BmudVWNqVhb4yOZ=Jkym4A@mail.gmail.com>

Congrats on the release! And thank you very much to all those who were
involved in making it happen (and Adrin in particular)!

-- 
Olivier

From godefroi.catherine at gmail.com  Wed May 13 13:15:38 2020
From: godefroi.catherine at gmail.com (Compte.validation)
Date: Wed, 13 May 2020 19:15:38 +0200
Subject: [scikit-learn] ANN scikit-learn 0.23.0 release
In-Reply-To: <CAFvE7K52tt8T5BpaecwQs8gG54-=BmudVWNqVhb4yOZ=Jkym4A@mail.gmail.com>
References: <CAEOrW49nzVm5h41xwnd74f10eMvSYuB6g1Uh3DwYvzwgX0o03w@mail.gmail.com>
 <CAFvE7K52tt8T5BpaecwQs8gG54-=BmudVWNqVhb4yOZ=Jkym4A@mail.gmail.com>
Message-ID: <CACy3+TqBv63tDASFck9XUHimF8fkFOybB+nAc8tvsaGQFDjsyA@mail.gmail.com>

STOP MAILING ME

Le mer. 13 mai 2020 ? 17:23, Olivier Grisel <olivier.grisel at ensta.org> a
?crit :

> Congrats on the release! And thank you very much to all those who were
> involved in making it happen (and Adrin in particular)!
>
> --
> Olivier
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200513/f1abefeb/attachment.html>

From vimalthilak at gmail.com  Wed May 13 13:32:27 2020
From: vimalthilak at gmail.com (Vimal Thilak)
Date: Wed, 13 May 2020 10:32:27 -0700
Subject: [scikit-learn] ANN scikit-learn 0.23.0 release
In-Reply-To: <CACy3+TqBv63tDASFck9XUHimF8fkFOybB+nAc8tvsaGQFDjsyA@mail.gmail.com>
References: <CAEOrW49nzVm5h41xwnd74f10eMvSYuB6g1Uh3DwYvzwgX0o03w@mail.gmail.com>
 <CAFvE7K52tt8T5BpaecwQs8gG54-=BmudVWNqVhb4yOZ=Jkym4A@mail.gmail.com>
 <CACy3+TqBv63tDASFck9XUHimF8fkFOybB+nAc8tvsaGQFDjsyA@mail.gmail.com>
Message-ID: <CALgtOETFBGvGTnF0Qc+PRn-qPV2AD5HjBUxzbsmZsFCtnqtFuA@mail.gmail.com>

On Wed, May 13, 2020 at 10:17 AM Compte.validation <
godefroi.catherine at gmail.com> wrote:

> STOP MAILING ME
>
This mailing list is an opt-in and as such you have complete freedom to
opt-out via https://mail.python.org/mailman/listinfo/scikit-learn

Best,


>
> Le mer. 13 mai 2020 ? 17:23, Olivier Grisel <olivier.grisel at ensta.org> a
> ?crit :
>
>> Congrats on the release! And thank you very much to all those who were
>> involved in making it happen (and Adrin in particular)!
>>
>> --
>> Olivier
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200513/6fd24f5c/attachment.html>

From t3kcit at gmail.com  Tue May 19 12:43:54 2020
From: t3kcit at gmail.com (t3kcit at gmail.com)
Date: Tue, 19 May 2020 12:43:54 -0400
Subject: [scikit-learn] major league hacking summer internship program
Message-ID: <066301d62dfc$af92cb90$0eb862b0$@gmail.com>

Hey Folks.

This program reached out to me:

https://news.mlh.io/mlh-fellowship-the-future-of-tech-internships-05-04-2020

 

What do you think?

Sounds like GSOC but with extra mentorship, so it might be a good fit for
us?

I would say it depends on what level of involvement they require from us.

 

Best,

Andy

 

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200519/4b67060e/attachment.html>

From marmochiaskl at gmail.com  Tue May 19 13:49:50 2020
From: marmochiaskl at gmail.com (Chiara Marmo)
Date: Tue, 19 May 2020 19:49:50 +0200
Subject: [scikit-learn] Notes core-dev meeting May 25th
Message-ID: <CAGfF149Z-wSrQ7sAKc37Tt5r9gYzM2XV3bvo-arzvCdopfRM-A@mail.gmail.com>

Dear core-devs,

I've taken the liberty to build a skeleton for the meeting notes.
Please, have a look and let me know if this is useful.

For each section, please, consider to add no more than two issues or PRs
per person to be discussed, this will allow everybody speak and close the
meeting in a reasonable time.
Also, it could be really useful to have all the contributions before the
week-end as everybody could take a look to the referenced discussions.

A more detailed reminder will be sent soon with a call for contribution
from the community and the details for the connection.

Thanks for your attention.

Best,
Chiara
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200519/9e0b29c5/attachment.html>

From adrin.jalali at gmail.com  Tue May 19 15:32:52 2020
From: adrin.jalali at gmail.com (Adrin)
Date: Tue, 19 May 2020 21:32:52 +0200
Subject: [scikit-learn] ANN: scikit-learn 0.23.1 release
Message-ID: <CAEOrW48AF_rJ7yQ4nL4YiN5LXEa85hNRTAG3gAFoeOcxwSoG7g@mail.gmail.com>

We're happy to announce the 0.23.1 release which fixes a few issues
affecting many users, namely: K-Means should be faster for small sample
sizes, and the representation of third-party estimators was fixed.

You can check this version out using:

    pip install -U scikit-learn

You can see the changelog here:
https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-1

The conda-forge builds will be available shortly, which you can then
install using:

    conda install -c conda-forge scikit-learn

On behalf of the scikit-learn development community,
Adrin
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200519/efe1c234/attachment.html>

From adrin.jalali at gmail.com  Tue May 19 15:39:21 2020
From: adrin.jalali at gmail.com (Adrin)
Date: Tue, 19 May 2020 21:39:21 +0200
Subject: [scikit-learn] Notes core-dev meeting May 25th
In-Reply-To: <CAGfF149Z-wSrQ7sAKc37Tt5r9gYzM2XV3bvo-arzvCdopfRM-A@mail.gmail.com>
References: <CAGfF149Z-wSrQ7sAKc37Tt5r9gYzM2XV3bvo-arzvCdopfRM-A@mail.gmail.com>
Message-ID: <CAEOrW48mqd=DoBd9JS49JwVbzaRmZEw75gN4KPkL_nZ0yh4Trw@mail.gmail.com>

Thanks Chiara,

I think I'm missing the link to the agenda. Where should I find it?

Thanks,
Adrin

On Tue, May 19, 2020 at 7:51 PM Chiara Marmo <marmochiaskl at gmail.com> wrote:

> Dear core-devs,
>
> I've taken the liberty to build a skeleton for the meeting notes.
> Please, have a look and let me know if this is useful.
>
> For each section, please, consider to add no more than two issues or PRs
> per person to be discussed, this will allow everybody speak and close the
> meeting in a reasonable time.
> Also, it could be really useful to have all the contributions before the
> week-end as everybody could take a look to the referenced discussions.
>
> A more detailed reminder will be sent soon with a call for contribution
> from the community and the details for the connection.
>
> Thanks for your attention.
>
> Best,
> Chiara
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200519/505c8781/attachment.html>

From niourf at gmail.com  Tue May 19 15:41:07 2020
From: niourf at gmail.com (Nicolas Hug)
Date: Tue, 19 May 2020 15:41:07 -0400
Subject: [scikit-learn] Notes core-dev meeting May 25th
In-Reply-To: <CAEOrW48mqd=DoBd9JS49JwVbzaRmZEw75gN4KPkL_nZ0yh4Trw@mail.gmail.com>
References: <CAGfF149Z-wSrQ7sAKc37Tt5r9gYzM2XV3bvo-arzvCdopfRM-A@mail.gmail.com>
 <CAEOrW48mqd=DoBd9JS49JwVbzaRmZEw75gN4KPkL_nZ0yh4Trw@mail.gmail.com>
Message-ID: <05bef331-9f89-5012-3e2a-cd2e65db1bd6@gmail.com>

https://hackmd.io/4VeWX5H9Tlmz132WAD-Q0w

On 5/19/20 3:39 PM, Adrin wrote:
> Thanks Chiara,
>
> I think I'm missing the link to the agenda. Where should I find it?
>
> Thanks,
> Adrin
>
> On Tue, May 19, 2020 at 7:51 PM Chiara Marmo <marmochiaskl at gmail.com 
> <mailto:marmochiaskl at gmail.com>> wrote:
>
>     Dear core-devs,
>
>     I've taken the liberty to build a skeleton for the meeting notes.
>     Please, have a look and let me know if this is useful.
>
>     For each section, please, consider to add no more than two issues
>     or PRs per person to be discussed, this will allow everybody speak
>     and close the meeting in a reasonable time.
>     Also, it could be really useful to have all the contributions
>     before the week-end as everybody could take a look to the
>     referenced discussions.
>
>     A more detailed reminder will be sent soon with a call for
>     contribution from the community and the details for the connection.
>
>     Thanks for your attention.
>
>     Best,
>     Chiara
>     _______________________________________________
>     scikit-learn mailing list
>     scikit-learn at python.org <mailto:scikit-learn at python.org>
>     https://mail.python.org/mailman/listinfo/scikit-learn
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200519/1793f7fe/attachment-0001.html>

From adrin.jalali at gmail.com  Tue May 19 15:42:26 2020
From: adrin.jalali at gmail.com (Adrin)
Date: Tue, 19 May 2020 21:42:26 +0200
Subject: [scikit-learn] major league hacking summer internship program
In-Reply-To: <066301d62dfc$af92cb90$0eb862b0$@gmail.com>
References: <066301d62dfc$af92cb90$0eb862b0$@gmail.com>
Message-ID: <CAEOrW4_dTjKacpC1KsG-BXQPJXtkqVFYC8_KZ5zkNr31GO8PEw@mail.gmail.com>

Sounds pretty cool to me.

On Tue, May 19, 2020 at 6:45 PM <t3kcit at gmail.com> wrote:

> Hey Folks.
>
> This program reached out to me:
>
>
> https://news.mlh.io/mlh-fellowship-the-future-of-tech-internships-05-04-2020
>
>
>
> What do you think?
>
> Sounds like GSOC but with extra mentorship, so it might be a good fit for
> us?
>
> I would say it depends on what level of involvement they require from us.
>
>
>
> Best,
>
> Andy
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200519/5fac4cf7/attachment.html>

From marmochiaskl at gmail.com  Tue May 19 16:58:41 2020
From: marmochiaskl at gmail.com (Chiara Marmo)
Date: Tue, 19 May 2020 22:58:41 +0200
Subject: [scikit-learn] Notes core-dev meeting May 25th
In-Reply-To: <05bef331-9f89-5012-3e2a-cd2e65db1bd6@gmail.com>
References: <CAGfF149Z-wSrQ7sAKc37Tt5r9gYzM2XV3bvo-arzvCdopfRM-A@mail.gmail.com>
 <CAEOrW48mqd=DoBd9JS49JwVbzaRmZEw75gN4KPkL_nZ0yh4Trw@mail.gmail.com>
 <05bef331-9f89-5012-3e2a-cd2e65db1bd6@gmail.com>
Message-ID: <CAGfF14_g=xH8T3j9aRTCYfQYioTrcenpoZXnwoi7W=6jaY2RXg@mail.gmail.com>

Thanks Nicolas for answering.

I think I'm missing the link to the agenda. Where should I find it?
>
> Adrin, just as a side note, you could also find the address of the pad in
the invitations I've sent for the meeting.
Best,
Chiara
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200519/39d175e9/attachment.html>

From godefroi.catherine at gmail.com  Tue May 19 17:17:34 2020
From: godefroi.catherine at gmail.com (Compte.validation)
Date: Tue, 19 May 2020 23:17:34 +0200
Subject: [scikit-learn] Notes core-dev meeting May 25th
In-Reply-To: <CAGfF14_g=xH8T3j9aRTCYfQYioTrcenpoZXnwoi7W=6jaY2RXg@mail.gmail.com>
References: <CAGfF149Z-wSrQ7sAKc37Tt5r9gYzM2XV3bvo-arzvCdopfRM-A@mail.gmail.com>
 <CAEOrW48mqd=DoBd9JS49JwVbzaRmZEw75gN4KPkL_nZ0yh4Trw@mail.gmail.com>
 <05bef331-9f89-5012-3e2a-cd2e65db1bd6@gmail.com>
 <CAGfF14_g=xH8T3j9aRTCYfQYioTrcenpoZXnwoi7W=6jaY2RXg@mail.gmail.com>
Message-ID: <CACy3+Tp34qb41umVuQm9wDJYadsZJnV3ESutcP1EBVT+yrteDQ@mail.gmail.com>

STOP MAIL

Le mar. 19 mai 2020 ? 23:00, Chiara Marmo <marmochiaskl at gmail.com> a ?crit :

> Thanks Nicolas for answering.
>
> I think I'm missing the link to the agenda. Where should I find it?
>>
>> Adrin, just as a side note, you could also find the address of the pad in
> the invitations I've sent for the meeting.
> Best,
> Chiara
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200519/b621fcd8/attachment.html>

From marmochiaskl at gmail.com  Fri May 22 05:10:55 2020
From: marmochiaskl at gmail.com (Chiara Marmo)
Date: Fri, 22 May 2020 11:10:55 +0200
Subject: [scikit-learn] scikit-learn monthly meeting May 25th
Message-ID: <CAGfF149O9uDhnqxZf-oeOX3B+W+28KyhbPM7WkO3TDkhW-w4Xg@mail.gmail.com>

Hi all,

The next scikit-learn monthly meeting will take place on Monday May 25th at
12PM UTC:
https://www.timeanddate.com/worldclock/meetingdetails.html?year=2020&month=5&day=25&hour=12&min=0&sec=0&p1=240&p2=33&p3=37&p4=179&p5=195

While these meetings are mainly for core-devs to discuss the current
topics, we're also happy to welcome non-core devs and other projects
maintainers! Feel free to join, using the following link:

https://meet.google.com/xhq-yoga-rtf

 <https://anaconda.zoom.us/j/94399382811?pwd=cXBtQ2lTVEtVbFpVTkE3TVFxdEhqZz09>

If you plan to attend and you would like to discuss something specific
about your contribution please add your name (or github pseudo) in the "
Contributors <https://hackmd.io/4VeWX5H9Tlmz132WAD-Q0w#Contributors>"
section, of the public pad:

https://hackmd.io/4VeWX5H9Tlmz132WAD-Q0w
<https://hackmd.io/5c6LxpnWSzeaBwJfuX5gPA>



*@core devs, please make sure to update your notes before the week-end.*


Best,

Chiara
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200522/55da88e9/attachment.html>

From niourf at gmail.com  Fri May 22 07:32:41 2020
From: niourf at gmail.com (Nicolas Hug)
Date: Fri, 22 May 2020 07:32:41 -0400
Subject: [scikit-learn] scikit-learn monthly meeting May 25th
In-Reply-To: <CAGfF149O9uDhnqxZf-oeOX3B+W+28KyhbPM7WkO3TDkhW-w4Xg@mail.gmail.com>
References: <CAGfF149O9uDhnqxZf-oeOX3B+W+28KyhbPM7WkO3TDkhW-w4Xg@mail.gmail.com>
Message-ID: <97816aab-adbd-f20d-f50b-fa20b4e83a70@gmail.com>

These were last month notes ;)? (the text of the link was correct, but 
the href wasn't)

The new pad is at https://hackmd.io/4VeWX5H9Tlmz132WAD-Q0w


On 5/22/20 5:10 AM, Chiara Marmo wrote:
>
> Hi all,
>
> The next scikit-learn monthly meeting will take place on Monday May 
> 25th at 12PM UTC: 
> https://www.timeanddate.com/worldclock/meetingdetails.html?year=2020&month=5&day=25&hour=12&min=0&sec=0&p1=240&p2=33&p3=37&p4=179&p5=195
>
> While these meetings are mainly for core-devs to discuss the current 
> topics, we're also happy to welcome non-core devs and other projects 
> maintainers! Feel free to join, using the following link:
>
> https://meet.google.com/xhq-yoga-rtf
>
> If you plan to attend and you would like to discuss something specific 
> about your contribution please add your name (or github pseudo) in the 
> "Contributors <https://hackmd.io/4VeWX5H9Tlmz132WAD-Q0w#Contributors>" 
> section, of the public pad:
>
> https://hackmd.io/4VeWX5H9Tlmz132WAD-Q0w 
> <https://hackmd.io/5c6LxpnWSzeaBwJfuX5gPA>
>
>
> *@core devs, please make sure to update your notes before the week-end.
> *
>
>
> Best,
>
> Chiara
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200522/c9a677f0/attachment.html>

From andreasmuellerml at gmail.com  Mon May 25 16:41:48 2020
From: andreasmuellerml at gmail.com (Andreas C. Mueller)
Date: Mon, 25 May 2020 16:41:48 -0400
Subject: [scikit-learn] Welcome to the TC Adrin!
Message-ID: <001101d632d4$ea133070$be399150$@gmail.com>

Dear all!

 

It's my pleasure to announce that Adrin has been accepted into the Technical
Committee unanimously!

Thank you Adrin for all the hard work so far and for your contributions to
the community!

 

Best,

Andy

 

Ps: yes I have a new email address

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200525/6d874a44/attachment.html>

From adrin.jalali at gmail.com  Tue May 26 03:23:11 2020
From: adrin.jalali at gmail.com (Adrin)
Date: Tue, 26 May 2020 09:23:11 +0200
Subject: [scikit-learn] Welcome to the TC Adrin!
In-Reply-To: <001101d632d4$ea133070$be399150$@gmail.com>
References: <001101d632d4$ea133070$be399150$@gmail.com>
Message-ID: <CAEOrW4_-6oYPaw45zHzF0=BrX9=78Rk0dfCTN9vPiKoxB2UWTw@mail.gmail.com>

Hi,

Thank you all for all your support, patience, and your trust on me since I
started :)
I hope to serve the community well.

Best,
Adrin

On Mon, May 25, 2020 at 10:43 PM Andreas C. Mueller <
andreasmuellerml at gmail.com> wrote:

> Dear all!
>
>
>
> It?s my pleasure to announce that Adrin has been accepted into the
> Technical Committee unanimously!
>
> Thank you Adrin for all the hard work so far and for your contributions to
> the community!
>
>
>
> Best,
>
> Andy
>
>
>
> Ps: yes I have a new email address
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200526/2c98580c/attachment.html>

From g.lemaitre58 at gmail.com  Wed May 27 10:25:55 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Wed, 27 May 2020 16:25:55 +0200
Subject: [scikit-learn] Random Binning Features
In-Reply-To: <CAP+w2ZifQeQMDN0bmFSReFwkvgp-+vk9qo6D=1_XgqP6yczTgg@mail.gmail.com>
References: <CAP+w2ZifQeQMDN0bmFSReFwkvgp-+vk9qo6D=1_XgqP6yczTgg@mail.gmail.com>
Message-ID: <CACDxx9guY=Wku2-zBm0CGaMLuAC8=HULvZ3xOUYhy6p-dp3DhA@mail.gmail.com>

The algorithm in scikit-learn-extra are usually algorithms which did not
meet the inclusion criteria (too early publication, not enough citations,
etc.)
However, the code quality is as good and tested than scikit-learn (usually
they were PR in the main repository).
Doing in this manner allows us to find the impact of the algorithms in
practice and maybe considering waiving-up the inclusion criterion.


On Sat, 2 May 2020 at 06:59, sai_ng <jonpsy101 at gmail.com> wrote:

> Hey folks !
> Hope you're all doing well.
>
> I'm developing Random Fourier Feature implementation in c++ for a
> repository. Scikits implementation on RBFSampler has been really helpful,
> and I must say that I'm charmed but how compact, yet powerful each line of
> code is.
>
> I'm writing this mail because I couldn't find your implementation of
> Random Binning Features, is it under development?. I tried searching in the
> issues but, to no avail. I noticed you've put few of your algorithms on a
> different repository for ex:
> https://scikit-learn-extra.readthedocs.io/en/latest/generated/sklearn_extra.kernel_approximation.Fastfood.html.
>
>
> Overall, I'd like to know if it's under development or has there been any
> draft/proposal or is it already implemented. I'd greatly appreciate if you
> could point me to other sources (if not here) which have successfully
> implemented it in code (preferably python/c++)
>
> "Hit me back,
> Just to chat,
> Your biggest fan,
> This is stan"
> ~ Eminem: Stan
>
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200527/515ab499/attachment.html>

From g.lemaitre58 at gmail.com  Wed May 27 11:14:06 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Wed, 27 May 2020 17:14:06 +0200
Subject: [scikit-learn] Class weight SVC
In-Reply-To: <PR2PR03MB5291C94A829D8FDAFB4AB989DDD00@PR2PR03MB5291.eurprd03.prod.outlook.com>
References: <PR2PR03MB5291C94A829D8FDAFB4AB989DDD00@PR2PR03MB5291.eurprd03.prod.outlook.com>
Message-ID: <CACDxx9h356=kha6P51vNhfTEiz=C-jX3jq6dFkdvqDWxj=aFfg@mail.gmail.com>

I don't think that we rescale the sample_weight and therefore the results
should be different.

On Fri, 24 Apr 2020 at 12:41, Francesco basciani <faf96 at hotmail.it> wrote:

> Hi, i have a question regarding the class weights in SVC. I have an
> imbalanced binary classification problem. In my case the ratio between the
> positive class and the negative class is 4:1. I just want to know if
> setting class weight to:
>
> class_weight = {1: 0.25, 0: 1} is the same to setting it to:
>
> class_weight = {1: 1, 0: 4}.
>
>
>
> Because in my case i obtain differents results using the two definition of
> the class weight
>
>
>
> Inviato da Posta <https://go.microsoft.com/fwlink/?LinkId=550986> per
> Windows 10
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200527/66f8bafd/attachment.html>

From g.lemaitre58 at gmail.com  Wed May 27 11:53:15 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Wed, 27 May 2020 17:53:15 +0200
Subject: [scikit-learn] [GridSearchCV] Reduction of elapsed time at the
 second interation
In-Reply-To: <CACSmxPJkxZBUmQvpJSAukWNX3fmbRk96TkWZwLCB6BASTXQmJg@mail.gmail.com>
References: <CACSmxPJkxZBUmQvpJSAukWNX3fmbRk96TkWZwLCB6BASTXQmJg@mail.gmail.com>
Message-ID: <CACDxx9i1276QWa47w8sHh3cGotAKqibkNfRO025w_LLOd+_83A@mail.gmail.com>

Regarding scikit-learn, the only thing that we cache is the transformer
processing in the pipeline (see the memory parameter in Pipeline).

It seems that you are passing a different set of features at each
iteration. Is the number of features different?

On Sun, 29 Mar 2020 at 19:23, Pedro Cardoso <pedro.cardoso.code at gmail.com>
wrote:

> Hello fellows,
>
> i am knew at slkearn and I have a question about GridSearchCV:
>
> I am running the following code  at a jupyter notebook :
>
> ----------------------*code*-------------------------------
>
> opt_models = dict()
> for feature in [features1, features2, features3, features4]:
>     cmb = CMB(x_train, y_train, x_test, y_test, feature)
>     cmb.fit()
>     cmb.predict()
>     opt_models[str(feature)]=cmb.get_best_model()
>
> -------------------------------------------------------
>
> The CMB class is just a class that contains different classification
> models (SVC, decision tree, etc...). When cmb.fit() is running, a
> gridSearchCV is performed at the SVC model  (which is within the cmb
> instance) in order to tune the hyperparameters C, gamma, and kernel. The
> SCV model is implemented using the sklearn.svm.SVC class. Here is the
> output of the first and second iteration of the for loop:
>
> ---------------------*output*-------------------------------------
> -> 1st iteration
>
>
> Fitting 5 folds for each of 12 candidates, totalling 60 fits
>
> [Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
> [Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    6.1s
> [Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    6.1s
> [Parallel(n_jobs=-1)]: Done   3 tasks      | elapsed:    6.1s
> [Parallel(n_jobs=-1)]: Done   4 tasks      | elapsed:    6.2s
> [Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    6.2s
> [Parallel(n_jobs=-1)]: Done   6 tasks      | elapsed:    6.2s
> [Parallel(n_jobs=-1)]: Done   7 tasks      | elapsed:    6.2s
> [Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    6.2s
> [Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    6.2s
> [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    6.2s
> [Parallel(n_jobs=-1)]: Done  11 tasks      | elapsed:    6.2s
> [Parallel(n_jobs=-1)]: Done  12 tasks      | elapsed:    6.3s
> [Parallel(n_jobs=-1)]: Done  13 tasks      | elapsed:    6.3s
> [Parallel(n_jobs=-1)]: Done  14 tasks      | elapsed:    6.3s
> [Parallel(n_jobs=-1)]: Done  15 tasks      | elapsed:    6.4s
> [Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    6.4s
> [Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    6.4s
> [Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    6.4s
> [Parallel(n_jobs=-1)]: Done  19 tasks      | elapsed:    6.5s
> [Parallel(n_jobs=-1)]: Done  20 tasks      | elapsed:    6.5s
> [Parallel(n_jobs=-1)]: Done  21 tasks      | elapsed:    6.5s
> [Parallel(n_jobs=-1)]: Done  22 tasks      | elapsed:    6.6s
> [Parallel(n_jobs=-1)]: Done  23 tasks      | elapsed:    6.7s
> [Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    6.7s
> [Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    6.7s
> [Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    6.8s
> [Parallel(n_jobs=-1)]: Done  27 tasks      | elapsed:    6.8s
> [Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    6.9s
> [Parallel(n_jobs=-1)]: Done  29 tasks      | elapsed:    6.9s
> [Parallel(n_jobs=-1)]: Done  30 tasks      | elapsed:    6.9s
> [Parallel(n_jobs=-1)]: Done  31 tasks      | elapsed:    7.0s
> [Parallel(n_jobs=-1)]: Done  32 tasks      | elapsed:    7.0s
> [Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    7.0s
> [Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    7.0s
> [Parallel(n_jobs=-1)]: Done  35 tasks      | elapsed:    7.1s
> [Parallel(n_jobs=-1)]: Done  36 tasks      | elapsed:    7.1s
> [Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:    7.2s
> [Parallel(n_jobs=-1)]: Done  38 tasks      | elapsed:    7.2s
> [Parallel(n_jobs=-1)]: Done  39 tasks      | elapsed:    7.2s
> [Parallel(n_jobs=-1)]: Done  40 tasks      | elapsed:    7.2s
> [Parallel(n_jobs=-1)]: Done  41 tasks      | elapsed:    7.3s
> [Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    7.3s
> [Parallel(n_jobs=-1)]: Done  43 tasks      | elapsed:    7.3s
> [Parallel(n_jobs=-1)]: Done  44 tasks      | elapsed:    7.4s
> [Parallel(n_jobs=-1)]: Done  45 tasks      | elapsed:    7.4s
> [Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    7.5s
>
>
> -> 2nd iteration
>
> Fitting 5 folds for each of 12 candidates, totalling 60 fits
>
> [Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
> [Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.0s
> [Parallel(n_jobs=-1)]: Batch computation too fast (0.0260s.) Setting batch_size=14.
> [Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s
> [Parallel(n_jobs=-1)]: Done   3 tasks      | elapsed:    0.0s
> [Parallel(n_jobs=-1)]: Done   4 tasks      | elapsed:    0.0s
> [Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    0.0s
> [Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:    0.7s finished
>
>
> ---------------------------------------------------------------------------------------------------------------------
>
>
> As you can see, the first iteration gets a elapsed time much larger than
> the 2nd iteration. Does it make sense? I am afraid that the model  is doing
> some kind of cache or shortcut from the 1st iteration, and consequently
> could decrease the model training/performance? I already read the sklearn
> documentation and I didn't saw any warning/note about this kind of
> behaviour.
>
> Thank you very much for your time :)
>
>
>
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200527/2530fc55/attachment-0001.html>

From andreasmuellerml at gmail.com  Thu May 28 17:34:02 2020
From: andreasmuellerml at gmail.com (Andreas C. Mueller)
Date: Thu, 28 May 2020 17:34:02 -0400
Subject: [scikit-learn] major league hacking summer internship program
In-Reply-To: <CAEOrW4_dTjKacpC1KsG-BXQPJXtkqVFYC8_KZ5zkNr31GO8PEw@mail.gmail.com>
References: <066301d62dfc$af92cb90$0eb862b0$@gmail.com>
 <CAEOrW4_dTjKacpC1KsG-BXQPJXtkqVFYC8_KZ5zkNr31GO8PEw@mail.gmail.com>
Message-ID: <050001d63537$b4fdbe90$1ef93bb0$@gmail.com>

Hi Folks.

So this program sounds pretty cool. They preselected some people for an ML work group, who will be doing daily standups together and pair programming,
and who might move around between some related projects over the 12 weeks of the program.

They made sure to get a diverse set of students and they have an engineer that will supervised them.

They would probably have 2-3 students working on sklearn.

They don?t expect having one big feature but they do expect some guidance on what issues to work on.

Also, the program starts on Monday, and they start contributing to OSS projects about a week after that.

Ideally we?d tell them if we?re in or not before Monday, and have a tentative list of issues / projects.

 

What do you all think? Also, if we want to do it, who would have cycles for some reviewing?

This seems to be well organized and they seem to have put quite some thought into it, but we do need to do a little bit of work on our end.

I can try picking some issues but I probably can?t commit a lot of reviewing time.

 

Cheers,

Andy

 

 

From: scikit-learn <scikit-learn-bounces+t3kcit=gmail.com at python.org> On Behalf Of Adrin
Sent: Tuesday, May 19, 2020 3:42 PM
To: Scikit-learn mailing list <scikit-learn at python.org>
Subject: Re: [scikit-learn] major league hacking summer internship program

 

Sounds pretty cool to me.

 

On Tue, May 19, 2020 at 6:45 PM <t3kcit at gmail.com <mailto:t3kcit at gmail.com> > wrote:

Hey Folks.

This program reached out to me:

https://news.mlh.io/mlh-fellowship-the-future-of-tech-internships-05-04-2020

 

What do you think?

Sounds like GSOC but with extra mentorship, so it might be a good fit for us?

I would say it depends on what level of involvement they require from us.

 

Best,

Andy

 

_______________________________________________
scikit-learn mailing list
scikit-learn at python.org <mailto:scikit-learn at python.org> 
https://mail.python.org/mailman/listinfo/scikit-learn

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200528/0adb5006/attachment.html>

From cdwijayarathna at gmail.com  Fri May 29 01:03:12 2020
From: cdwijayarathna at gmail.com (Chamila Wijayarathna)
Date: Fri, 29 May 2020 10:33:12 +0530
Subject: [scikit-learn] sklearn Pipeline: argument of type
 'ColumnTransformer' is not iterable
Message-ID: <CAAGV2Dgw5K4zzV1F0TXo47eUUdRnLgsBWtYsW5Cqr3KoSkZo3w@mail.gmail.com>

Hello all,

I hope I am writing to the correct mailing list about this issue that I am
having. Please apologize me if I am not.

I am attempting to use a pipeline to feed an ensemble voting classifier as
I want the ensemble learner to use models that train on different feature
sets. For this purpose, I followed the tutorial available at [1].

Following is the code that I could develop so far.























*y = df1.indexx = preprocessing.scale(df1)phy_features = ['A', 'B',
'C']phy_transformer = Pipeline(steps=[('imputer',
SimpleImputer(strategy='median')), ('scaler',
StandardScaler())])phy_processer = ColumnTransformer(transformers=[('phy',
phy_transformer, phy_features)])fa_features = ['D', 'E', 'F']fa_transformer
= Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler',
StandardScaler())])fa_processer = ColumnTransformer(transformers=[('fa',
fa_transformer, fa_features)])pipe_phy = Pipeline(steps=[('preprocessor',
phy_processer ),('classifier', SVM)])pipe_fa =
Pipeline(steps=[('preprocessor', fa_processer ),('classifier', SVM)])ens =
VotingClassifier(estimators=[pipe_phy, pipe_fa])cv = KFold(n_splits=10,
random_state=None, shuffle=True)for train_index, test_index in
cv.split(x):    x_train, x_test = x[train_index], x[test_index]    y_train,
y_test = y[train_index], y[test_index]    ens.fit(x_train,y_train)
print(ens.score(x_test, y_test))*

However, when running the code, I am getting an error saying *TypeError:
argument of type 'ColumnTransformer' is not iterable*, at the line
*ens.fit(x_train,y_train).*

What is the reason for this and how can I fix it?

Thank you,
Chamila
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/14d70bc8/attachment.html>

From cdwijayarathna at gmail.com  Fri May 29 02:30:46 2020
From: cdwijayarathna at gmail.com (Chamila Wijayarathna)
Date: Fri, 29 May 2020 12:00:46 +0530
Subject: [scikit-learn] sklearn Pipeline: argument of type
 'ColumnTransformer' is not iterable
In-Reply-To: <CAAGV2Dgw5K4zzV1F0TXo47eUUdRnLgsBWtYsW5Cqr3KoSkZo3w@mail.gmail.com>
References: <CAAGV2Dgw5K4zzV1F0TXo47eUUdRnLgsBWtYsW5Cqr3KoSkZo3w@mail.gmail.com>
Message-ID: <CAAGV2DjywTF1pPHmutei8t75K0chpMv=kgpR2LDsWvw83hE1MA@mail.gmail.com>

Hi all,

I did manage to get the code to run using a workaround, which is bit ugly.

Following is the complete stacktrace of the error I was receiving.



















*Traceback (most recent call last):  File "<input>", line 1, in <module>
File "C:\Program Files\JetBrains\PyCharm
2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line
197, in runfile    pydev_imports.execfile(filename, global_vars,
local_vars)  # execute the script  File "C:\Program Files\JetBrains\PyCharm
2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line
18, in execfile    exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line 112, in
<module>    ens.fit(x_train,y_train)  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
line 265, in fit    return super().fit(X, transformed_y, sample_weight)
File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
line 65, in fit    names, clfs = self._validate_estimators()  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py",
line 228, in _validate_estimators    self._validate_names(names)  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py",
line 77, in _validate_names    invalid_names = [name for name in names if
'__' in name]  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py",
line 77, in <listcomp>    invalid_names = [name for name in names if '__'
in name]TypeError: argument of type 'ColumnTransformer' is not iterable*

Following are the inputs in 'names' list at the time of the error.

1-
*ColumnTransformer(transformers=[('phy', Pipeline(steps=[('imputer',
SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), ['HR',
'RMSSD', 'SCL'])])2-
ColumnTransformer(transformers=[('fa',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',
StandardScaler())]),['Squality', 'Sneutral', 'Shappy'])])*

Seems like that the library is attempting to search for '__' substring of
the ColumnTransform object, which it is unable to perform.

Since this name check doesn't have a signiticant effect on my
functionality, I commented following snippet at
*sklearn\utils\metaestimators.py.*




*invalid_names = [name for name in names if '__' in name]if invalid_names:
  raise ValueError('Estimator names must not contain __: got '
      '{0!r}'.format(invalid_names))*

Please let me know if there is a better workaround or that their are any
issues of commenting out this code.

Thanks

On Fri, May 29, 2020 at 10:33 AM Chamila Wijayarathna <
cdwijayarathna at gmail.com> wrote:

> Hello all,
>
> I hope I am writing to the correct mailing list about this issue that I am
> having. Please apologize me if I am not.
>
> I am attempting to use a pipeline to feed an ensemble voting classifier as
> I want the ensemble learner to use models that train on different feature
> sets. For this purpose, I followed the tutorial available at [1].
>
> Following is the code that I could develop so far.
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> *y = df1.indexx = preprocessing.scale(df1)phy_features = ['A', 'B',
> 'C']phy_transformer = Pipeline(steps=[('imputer',
> SimpleImputer(strategy='median')), ('scaler',
> StandardScaler())])phy_processer = ColumnTransformer(transformers=[('phy',
> phy_transformer, phy_features)])fa_features = ['D', 'E', 'F']fa_transformer
> = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler',
> StandardScaler())])fa_processer = ColumnTransformer(transformers=[('fa',
> fa_transformer, fa_features)])pipe_phy = Pipeline(steps=[('preprocessor',
> phy_processer ),('classifier', SVM)])pipe_fa =
> Pipeline(steps=[('preprocessor', fa_processer ),('classifier', SVM)])ens =
> VotingClassifier(estimators=[pipe_phy, pipe_fa])cv = KFold(n_splits=10,
> random_state=None, shuffle=True)for train_index, test_index in
> cv.split(x):    x_train, x_test = x[train_index], x[test_index]    y_train,
> y_test = y[train_index], y[test_index]    ens.fit(x_train,y_train)
> print(ens.score(x_test, y_test))*
>
> However, when running the code, I am getting an error saying *TypeError:
> argument of type 'ColumnTransformer' is not iterable*, at the line
> *ens.fit(x_train,y_train).*
>
> What is the reason for this and how can I fix it?
>
> Thank you,
> Chamila
>


-- 
Chamila Dilshan Wijayarathna,
PhD Research Student
The University of New South Wales (UNSW Canberra)
Australian Centre for Cyber Security
Australian Defence Force Academy
PO Box 7916, Canberra BA ACT 2610
Australia
Mobile:(+61)416895795
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/7586b212/attachment-0001.html>

From adrin.jalali at gmail.com  Fri May 29 05:40:48 2020
From: adrin.jalali at gmail.com (Adrin)
Date: Fri, 29 May 2020 11:40:48 +0200
Subject: [scikit-learn] major league hacking summer internship program
In-Reply-To: <050001d63537$b4fdbe90$1ef93bb0$@gmail.com>
References: <066301d62dfc$af92cb90$0eb862b0$@gmail.com>
 <CAEOrW4_dTjKacpC1KsG-BXQPJXtkqVFYC8_KZ5zkNr31GO8PEw@mail.gmail.com>
 <050001d63537$b4fdbe90$1ef93bb0$@gmail.com>
Message-ID: <CAEOrW48+-4NmQnJ24RBkDDh=ef79KOdOTY_x_KuXTLn_aOjXyQ@mail.gmail.com>

Thanks Andy, sounds pretty cool.

I can commit some reviewing time. There should be maybe two of us at least
that they know they can ping, and we can ping others if needed.

Cheers,
Adrin

On Thu, May 28, 2020 at 11:35 PM Andreas C. Mueller <
andreasmuellerml at gmail.com> wrote:

> Hi Folks.
>
> So this program sounds pretty cool. They preselected some people for an ML
> work group, who will be doing daily standups together and pair programming,
> and who might move around between some related projects over the 12 weeks
> of the program.
>
> They made sure to get a diverse set of students and they have an engineer
> that will supervised them.
>
> They would probably have 2-3 students working on sklearn.
>
> They don?t expect having one big feature but they do expect some guidance
> on what issues to work on.
>
> Also, the program starts on Monday, and they start contributing to OSS
> projects about a week after that.
>
> Ideally we?d tell them if we?re in or not before Monday, and have a
> tentative list of issues / projects.
>
>
>
> What do you all think? Also, if we want to do it, who would have cycles
> for some reviewing?
>
> This seems to be well organized and they seem to have put quite some
> thought into it, but we do need to do a little bit of work on our end.
>
> I can try picking some issues but I probably can?t commit a lot of
> reviewing time.
>
>
>
> Cheers,
>
> Andy
>
>
>
>
>
> *From:* scikit-learn <scikit-learn-bounces+t3kcit=gmail.com at python.org> *On
> Behalf Of *Adrin
> *Sent:* Tuesday, May 19, 2020 3:42 PM
> *To:* Scikit-learn mailing list <scikit-learn at python.org>
> *Subject:* Re: [scikit-learn] major league hacking summer internship
> program
>
>
>
> Sounds pretty cool to me.
>
>
>
> On Tue, May 19, 2020 at 6:45 PM <t3kcit at gmail.com> wrote:
>
> Hey Folks.
>
> This program reached out to me:
>
>
> https://news.mlh.io/mlh-fellowship-the-future-of-tech-internships-05-04-2020
>
>
>
> What do you think?
>
> Sounds like GSOC but with extra mentorship, so it might be a good fit for
> us?
>
> I would say it depends on what level of involvement they require from us.
>
>
>
> Best,
>
> Andy
>
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/c2a55036/attachment.html>

From g.lemaitre58 at gmail.com  Fri May 29 05:48:24 2020
From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Fri, 29 May 2020 11:48:24 +0200
Subject: [scikit-learn] major league hacking summer internship program
In-Reply-To: <CAEOrW48+-4NmQnJ24RBkDDh=ef79KOdOTY_x_KuXTLn_aOjXyQ@mail.gmail.com>
References: <066301d62dfc$af92cb90$0eb862b0$@gmail.com>
 <CAEOrW4_dTjKacpC1KsG-BXQPJXtkqVFYC8_KZ5zkNr31GO8PEw@mail.gmail.com>
 <050001d63537$b4fdbe90$1ef93bb0$@gmail.com>
 <CAEOrW48+-4NmQnJ24RBkDDh=ef79KOdOTY_x_KuXTLn_aOjXyQ@mail.gmail.com>
Message-ID: <CACDxx9jK0KvqRRPqWuDJt1W6ns+3hMBH_G78oZns8NTRE0RmCg@mail.gmail.com>

Hey,

I can dedicate some time to review.

Cheers,

On Fri, 29 May 2020 at 11:43, Adrin <adrin.jalali at gmail.com> wrote:

> Thanks Andy, sounds pretty cool.
>
> I can commit some reviewing time. There should be maybe two of us at least
> that they know they can ping, and we can ping others if needed.
>
> Cheers,
> Adrin
>
> On Thu, May 28, 2020 at 11:35 PM Andreas C. Mueller <
> andreasmuellerml at gmail.com> wrote:
>
>> Hi Folks.
>>
>> So this program sounds pretty cool. They preselected some people for an
>> ML work group, who will be doing daily standups together and pair
>> programming,
>> and who might move around between some related projects over the 12 weeks
>> of the program.
>>
>> They made sure to get a diverse set of students and they have an engineer
>> that will supervised them.
>>
>> They would probably have 2-3 students working on sklearn.
>>
>> They don?t expect having one big feature but they do expect some guidance
>> on what issues to work on.
>>
>> Also, the program starts on Monday, and they start contributing to OSS
>> projects about a week after that.
>>
>> Ideally we?d tell them if we?re in or not before Monday, and have a
>> tentative list of issues / projects.
>>
>>
>>
>> What do you all think? Also, if we want to do it, who would have cycles
>> for some reviewing?
>>
>> This seems to be well organized and they seem to have put quite some
>> thought into it, but we do need to do a little bit of work on our end.
>>
>> I can try picking some issues but I probably can?t commit a lot of
>> reviewing time.
>>
>>
>>
>> Cheers,
>>
>> Andy
>>
>>
>>
>>
>>
>> *From:* scikit-learn <scikit-learn-bounces+t3kcit=gmail.com at python.org> *On
>> Behalf Of *Adrin
>> *Sent:* Tuesday, May 19, 2020 3:42 PM
>> *To:* Scikit-learn mailing list <scikit-learn at python.org>
>> *Subject:* Re: [scikit-learn] major league hacking summer internship
>> program
>>
>>
>>
>> Sounds pretty cool to me.
>>
>>
>>
>> On Tue, May 19, 2020 at 6:45 PM <t3kcit at gmail.com> wrote:
>>
>> Hey Folks.
>>
>> This program reached out to me:
>>
>>
>> https://news.mlh.io/mlh-fellowship-the-future-of-tech-internships-05-04-2020
>>
>>
>>
>> What do you think?
>>
>> Sounds like GSOC but with extra mentorship, so it might be a good fit for
>> us?
>>
>> I would say it depends on what level of involvement they require from us.
>>
>>
>>
>> Best,
>>
>> Andy
>>
>>
>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/19d8b499/attachment-0001.html>

From thomasjpfan at gmail.com  Fri May 29 09:44:16 2020
From: thomasjpfan at gmail.com (Thomas J Fan)
Date: Fri, 29 May 2020 09:44:16 -0400
Subject: [scikit-learn] major league hacking summer internship program
In-Reply-To: <dff077b6-b63b-47ff-b070-246c2c2de64d@Canary>
References: <066301d62dfc$af92cb90$0eb862b0$@gmail.com>
 <CAEOrW4_dTjKacpC1KsG-BXQPJXtkqVFYC8_KZ5zkNr31GO8PEw@mail.gmail.com>
 <050001d63537$b4fdbe90$1ef93bb0$@gmail.com>
 <dff077b6-b63b-47ff-b070-246c2c2de64d@Canary>
Message-ID: <dff077b6-b63b-47ff-b070-246c2c2de64d@Canary>

I can commit to reviewing. Diving into their program, it looks like they are hiring supervisers through: https://raise.dev/Apply/?ref=mlh which is titled "Software Developer Coach". By looking at their https://fellowship.mlh.io/students they have about 9 weeks of actual contributing.

Given they have an engineer to help, maybe they can work on the documenting the production aspects:

1. Roadmap item 19: Documentation and tooling for model lifecycle management
2. Roadmap item 21: Document good practices to detect temporal distrubiton drift

Regards,
Thomas

> On Thursday, May 28, 2020 at 5:36 PM, Andreas C. Mueller <andreasmuellerml at gmail.com (mailto:andreasmuellerml at gmail.com)> wrote:
>
> Hi Folks.
>
>
> So this program sounds pretty cool. They preselected some people for an ML work group, who will be doing daily standups together and pair programming,
> and who might move around between some related projects over the 12 weeks of the program.
>
>
> They made sure to get a diverse set of students and they have an engineer that will supervised them.
>
>
> They would probably have 2-3 students working on sklearn.
>
>
> They don?t expect having one big feature but they do expect some guidance on what issues to work on.
>
>
> Also, the program starts on Monday, and they start contributing to OSS projects about a week after that.
>
>
> Ideally we?d tell them if we?re in or not before Monday, and have a tentative list of issues / projects.
>
>
>
>
>
> What do you all think? Also, if we want to do it, who would have cycles for some reviewing?
>
>
> This seems to be well organized and they seem to have put quite some thought into it, but we do need to do a little bit of work on our end.
>
>
> I can try picking some issues but I probably can?t commit a lot of reviewing time.
>
>
>
>
>
> Cheers,
>
>
> Andy
>
>
>
>
>
>
>
>
> From: scikit-learn <scikit-learn-bounces+t3kcit=gmail.com at python.org> On Behalf Of Adrin
> Sent: Tuesday, May 19, 2020 3:42 PM
> To: Scikit-learn mailing list <scikit-learn at python.org>
> Subject: Re: [scikit-learn] major league hacking summer internship program
>
>
>
>
>
>
>
> Sounds pretty cool to me.
>
>
>
>
>
>
>
> On Tue, May 19, 2020 at 6:45 PM <t3kcit at gmail.com (mailto:t3kcit at gmail.com)> wrote:
>
>
> >
> > Hey Folks.
> >
> >
> > This program reached out to me:
> >
> >
> > https://news.mlh.io/mlh-fellowship-the-future-of-tech-internships-05-04-2020
> >
> >
> >
> >
> >
> > What do you think?
> >
> >
> > Sounds like GSOC but with extra mentorship, so it might be a good fit for us?
> >
> >
> > I would say it depends on what level of involvement they require from us.
> >
> >
> >
> >
> >
> > Best,
> >
> >
> > Andy
> >
> >
> >
> >
> >
> >
> >
> > _______________________________________________
> > scikit-learn mailing list
> > scikit-learn at python.org (mailto:scikit-learn at python.org)
> > https://mail.python.org/mailman/listinfo/scikit-learn
> >
>
>
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/d4ed8a4d/attachment.html>

From thomasjpfan at gmail.com  Fri May 29 10:02:26 2020
From: thomasjpfan at gmail.com (Thomas J Fan)
Date: Fri, 29 May 2020 10:02:26 -0400
Subject: [scikit-learn] sklearn Pipeline: argument of type
 'ColumnTransformer' is not iterable
In-Reply-To: <CAAGV2DjywTF1pPHmutei8t75K0chpMv=kgpR2LDsWvw83hE1MA@mail.gmail.com>
References: <CAAGV2Dgw5K4zzV1F0TXo47eUUdRnLgsBWtYsW5Cqr3KoSkZo3w@mail.gmail.com>
 <CAAGV2DjywTF1pPHmutei8t75K0chpMv=kgpR2LDsWvw83hE1MA@mail.gmail.com>
Message-ID: <52354c90-c217-42e4-86d7-77d1eb8c30a9@Canary>

VotingClassifer also needs names:

ens = VotingClassifier(estimators=[('pipe1', pipe_phy), ('pipe2', pipe_fa)])

Thomas

> On Friday, May 29, 2020 at 2:33 AM, Chamila Wijayarathna <cdwijayarathna at gmail.com (mailto:cdwijayarathna at gmail.com)> wrote:
> Hi all,
>
> I did manage to get the code to run using a workaround, which is bit ugly.
>
> Following is the complete stacktrace of the error I was receiving.
>
> Traceback (most recent call last):
> File "<input>", line 1, in <module>
> File "C:\Program Files\JetBrains\PyCharm 2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
> pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
> File "C:\Program Files\JetBrains\PyCharm 2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
> exec(compile(contents+"\n", file, 'exec'), glob, loc)
> File "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line 112, in <module>
> ens.fit(x_train,y_train)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py", line 265, in fit
> return super().fit(X, transformed_y, sample_weight)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py", line 65, in fit
> names, clfs = self._validate_estimators()
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py", line 228, in _validate_estimators
> self._validate_names(names)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py", line 77, in _validate_names
> invalid_names = [name for name in names if '__' in name]
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py", line 77, in <listcomp>
> invalid_names = [name for name in names if '__' in name]
> TypeError: argument of type 'ColumnTransformer' is not iterable
>
> Following are the inputs in 'names' list at the time of the error.
>
> 1- ColumnTransformer(transformers=[('phy', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), ['HR', 'RMSSD', 'SCL'])])
> 2- ColumnTransformer(transformers=[('fa',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler())]),['Squality', 'Sneutral', 'Shappy'])])
>
> Seems like that the library is attempting to search for '__' substring of the ColumnTransform object, which it is unable to perform.
>
> Since this name check doesn't have a signiticant effect on my functionality, I commented following snippet at sklearn\utils\metaestimators.py.
>
> invalid_names = [name for name in names if '__' in name]
> if invalid_names:
> raise ValueError('Estimator names must not contain __: got '
> '{0!r}'.format(invalid_names))
>
> Please let me know if there is a better workaround or that their are any issues of commenting out this code.
>
> Thanks
> On Fri, May 29, 2020 at 10:33 AM Chamila Wijayarathna <cdwijayarathna at gmail.com (mailto:cdwijayarathna at gmail.com)> wrote:
> > Hello all,
> >
> > I hope I am writing to the correct mailing list about this issue that I am having. Please apologize me if I am not.
> >
> > I am attempting to use a pipeline to feed an ensemble voting classifier as I want the ensemble learner to use models that train on different feature sets. For this purpose, I followed the tutorial available at [1].
> >
> > Following is the code that I could develop so far.
> >
> > y = df1.index
> > x = preprocessing.scale(df1)
> >
> > phy_features = ['A', 'B', 'C']
> > phy_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])
> > phy_processer = ColumnTransformer(transformers=[('phy', phy_transformer, phy_features)])
> >
> > fa_features = ['D', 'E', 'F']
> > fa_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])
> > fa_processer = ColumnTransformer(transformers=[('fa', fa_transformer, fa_features)])
> >
> >
> > pipe_phy = Pipeline(steps=[('preprocessor', phy_processer ),('classifier', SVM)])
> > pipe_fa = Pipeline(steps=[('preprocessor', fa_processer ),('classifier', SVM)])
> >
> > ens = VotingClassifier(estimators=[pipe_phy, pipe_fa])
> >
> > cv = KFold(n_splits=10, random_state=None, shuffle=True)
> > for train_index, test_index in cv.split(x):
> > x_train, x_test = x[train_index], x[test_index]
> > y_train, y_test = y[train_index], y[test_index]
> > ens.fit(x_train,y_train)
> > print(ens.score(x_test, y_test))
> >
> > However, when running the code, I am getting an error saying TypeError: argument of type 'ColumnTransformer' is not iterable, at the line ens.fit(x_train,y_train).
> >
> > What is the reason for this and how can I fix it?
> >
> > Thank you,
> > Chamila
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
>
>
>
>
> --
> Chamila Dilshan Wijayarathna,
> PhD Research Student
> The University of New South Wales (UNSW Canberra)
> Australian Centre for Cyber Security
> Australian Defence Force Academy
> PO Box 7916, Canberra BA ACT 2610
> Australia
> Mobile:(+61)416895795
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/e4874689/attachment-0001.html>

From andreasmuellerml at gmail.com  Fri May 29 10:21:55 2020
From: andreasmuellerml at gmail.com (Andreas Mueller)
Date: Fri, 29 May 2020 10:21:55 -0400
Subject: [scikit-learn] major league hacking summer internship program
In-Reply-To: <dff077b6-b63b-47ff-b070-246c2c2de64d@Canary>
References: <066301d62dfc$af92cb90$0eb862b0$@gmail.com>
 <CAEOrW4_dTjKacpC1KsG-BXQPJXtkqVFYC8_KZ5zkNr31GO8PEw@mail.gmail.com>
 <050001d63537$b4fdbe90$1ef93bb0$@gmail.com>
 <dff077b6-b63b-47ff-b070-246c2c2de64d@Canary>
 <dff077b6-b63b-47ff-b070-246c2c2de64d@Canary>
Message-ID: <CADhGEUd5JWz9X6TCapydTdoFqntjP7GBZ+Kr8EuvB-6r35RJ=g@mail.gmail.com>

Thanks folks! That gives us a good start I think!

Re documentation: honestly I'm not entirely sure if those are good issues
because I'm not sure if we have consensus what we want to recommend. We can
certainly include these but they require some decisions and a lot of
expertise. Maybe we can discuss further issues either here or on gitter?

Andy


On Fri, May 29, 2020, 09:45 Thomas J Fan <thomasjpfan at gmail.com> wrote:

> I can commit to reviewing. Diving into their program, it looks like they
> are hiring supervisers through: https://raise.dev/Apply/?ref=mlh which is
> titled "Software Developer Coach". By looking at their
> https://fellowship.mlh.io/students they have about 9 weeks of actual
> contributing.
>
> Given they have an engineer to help, maybe they can work on the
> documenting the production aspects:
>
> 1. Roadmap item 19: Documentation and tooling for model lifecycle
> management
> 2. Roadmap item 21: Document good practices to detect temporal distrubiton
> drift
>
> Regards,
> Thomas
>
> On Thursday, May 28, 2020 at 5:36 PM, Andreas C. Mueller <
> andreasmuellerml at gmail.com> wrote:
>
> Hi Folks.
>
> So this program sounds pretty cool. They preselected some people for an ML
> work group, who will be doing daily standups together and pair programming,
> and who might move around between some related projects over the 12 weeks
> of the program.
>
> They made sure to get a diverse set of students and they have an engineer
> that will supervised them.
>
> They would probably have 2-3 students working on sklearn.
>
> They don?t expect having one big feature but they do expect some guidance
> on what issues to work on.
>
> Also, the program starts on Monday, and they start contributing to OSS
> projects about a week after that.
>
> Ideally we?d tell them if we?re in or not before Monday, and have a
> tentative list of issues / projects.
>
>
>
> What do you all think? Also, if we want to do it, who would have cycles
> for some reviewing?
>
> This seems to be well organized and they seem to have put quite some
> thought into it, but we do need to do a little bit of work on our end.
>
> I can try picking some issues but I probably can?t commit a lot of
> reviewing time.
>
>
>
> Cheers,
>
> Andy
>
>
>
>
>
> *From:* scikit-learn <scikit-learn-bounces+t3kcit=gmail.com at python.org> *On
> Behalf Of *Adrin
> *Sent:* Tuesday, May 19, 2020 3:42 PM
> *To:* Scikit-learn mailing list <scikit-learn at python.org>
> *Subject:* Re: [scikit-learn] major league hacking summer internship
> program
>
>
>
> Sounds pretty cool to me.
>
>
>
> On Tue, May 19, 2020 at 6:45 PM <t3kcit at gmail.com> wrote:
>
> Hey Folks.
>
> This program reached out to me:
>
>
> https://news.mlh.io/mlh-fellowship-the-future-of-tech-internships-05-04-2020
>
>
>
> What do you think?
>
> Sounds like GSOC but with extra mentorship, so it might be a good fit for
> us?
>
> I would say it depends on what level of involvement they require from us.
>
>
>
> Best,
>
> Andy
>
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/9fc86c94/attachment.html>

From cdwijayarathna at gmail.com  Fri May 29 11:42:56 2020
From: cdwijayarathna at gmail.com (Chamila Wijayarathna)
Date: Fri, 29 May 2020 21:12:56 +0530
Subject: [scikit-learn] sklearn Pipeline: argument of type
 'ColumnTransformer' is not iterable
In-Reply-To: <52354c90-c217-42e4-86d7-77d1eb8c30a9@Canary>
References: <CAAGV2Dgw5K4zzV1F0TXo47eUUdRnLgsBWtYsW5Cqr3KoSkZo3w@mail.gmail.com>
 <CAAGV2DjywTF1pPHmutei8t75K0chpMv=kgpR2LDsWvw83hE1MA@mail.gmail.com>
 <52354c90-c217-42e4-86d7-77d1eb8c30a9@Canary>
Message-ID: <CAAGV2DjhVE6cCSpPyxsQPDp3+V2-_UkE+GBhrnmbvvrGZeDxPQ@mail.gmail.com>

Hi,

Thanks, this solution fixed the issue. However, it introduces a new error,
which was not there before.

Traceback (most recent call last):
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\__init__.py",
line 425, in _get_column_indices
    all_columns = X.columns
AttributeError: 'numpy.ndarray' object has no attribute 'columns'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
  File "<input>", line 1, in <module>
  File "C:\Program Files\JetBrains\PyCharm
2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line
197, in runfile
    pydev_imports.execfile(filename, global_vars, local_vars)  # execute
the script
  File "C:\Program Files\JetBrains\PyCharm
2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line
18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line 127, in
<module>
    ens.fit(x_train,y_train)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
line 265, in fit
    return super().fit(X, transformed_y, sample_weight)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
line 81, in fit
    for idx, clf in enumerate(clfs) if clf not in (None, 'drop')
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
line 1029, in __call__
    if self.dispatch_one_batch(iterator):
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
line 847, in dispatch_one_batch
    self._dispatch(tasks)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
line 765, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\_parallel_backends.py",
line 206, in apply_async
    result = ImmediateResult(func)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\_parallel_backends.py",
line 570, in __init__
    self.results = batch()
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
line 253, in __call__
    for func, args, kwargs in self.items]
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
line 253, in <listcomp>
    for func, args, kwargs in self.items]
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py",
line 40, in _fit_single_estimator
    estimator.fit(X, y)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py",
line 330, in fit
    Xt = self._fit(X, y, **fit_params_steps)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py",
line 296, in _fit
    **fit_params_steps[name])
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\memory.py",
line 352, in __call__
    return self.func(*args, **kwargs)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py",
line 740, in _fit_transform_one
    res = transformer.fit_transform(X, y, **fit_params)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\compose\_column_transformer.py",
line 529, in fit_transform
    self._validate_remainder(X)
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\compose\_column_transformer.py",
line 327, in _validate_remainder
    cols.extend(_get_column_indices(X, columns))
  File
"C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\__init__.py",
line 427, in _get_column_indices
    raise ValueError("Specifying the columns using strings is only "
ValueError: Specifying the columns using strings is only supported for
pandas DataFrames

Thanks

On Fri, May 29, 2020 at 7:33 PM Thomas J Fan <thomasjpfan at gmail.com> wrote:

> VotingClassifer also needs names:
>
> ens = VotingClassifier(estimators=[('pipe1', pipe_phy), ('pipe2',
> pipe_fa)])
>
> Thomas
>
> On Friday, May 29, 2020 at 2:33 AM, Chamila Wijayarathna <
> cdwijayarathna at gmail.com> wrote:
> Hi all,
>
> I did manage to get the code to run using a workaround, which is bit ugly.
>
> Following is the complete stacktrace of the error I was receiving.
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> *Traceback (most recent call last):  File "<input>", line 1, in <module>
> File "C:\Program Files\JetBrains\PyCharm
> 2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line
> 197, in runfile    pydev_imports.execfile(filename, global_vars,
> local_vars)  # execute the script  File "C:\Program Files\JetBrains\PyCharm
> 2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line
> 18, in execfile    exec(compile(contents+"\n", file, 'exec'), glob, loc)
> File "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line 112, in
> <module>    ens.fit(x_train,y_train)  File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
> line 265, in fit    return super().fit(X, transformed_y, sample_weight)
> File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
> line 65, in fit    names, clfs = self._validate_estimators()  File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py",
> line 228, in _validate_estimators    self._validate_names(names)  File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py",
> line 77, in _validate_names    invalid_names = [name for name in names if
> '__' in name]  File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py",
> line 77, in <listcomp>    invalid_names = [name for name in names if '__'
> in name]TypeError: argument of type 'ColumnTransformer' is not iterable*
>
> Following are the inputs in 'names' list at the time of the error.
>
> 1-
> *ColumnTransformer(transformers=[('phy', Pipeline(steps=[('imputer',
> SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), ['HR',
> 'RMSSD', 'SCL'])])2-
> ColumnTransformer(transformers=[('fa',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',
> StandardScaler())]),['Squality', 'Sneutral', 'Shappy'])])*
>
> Seems like that the library is attempting to search for '__' substring of
> the ColumnTransform object, which it is unable to perform.
>
> Since this name check doesn't have a signiticant effect on my
> functionality, I commented following snippet at
> *sklearn\utils\metaestimators.py.*
>
>
>
>
> *invalid_names = [name for name in names if '__' in name]if
> invalid_names:    raise ValueError('Estimator names must not contain __:
> got '                    '{0!r}'.format(invalid_names))*
>
> Please let me know if there is a better workaround or that their are any
> issues of commenting out this code.
>
> Thanks
>
> On Fri, May 29, 2020 at 10:33 AM Chamila Wijayarathna <
> cdwijayarathna at gmail.com> wrote:
>
>> Hello all,
>>
>> I hope I am writing to the correct mailing list about this issue that I
>> am having. Please apologize me if I am not.
>>
>> I am attempting to use a pipeline to feed an ensemble voting classifier
>> as I want the ensemble learner to use models that train on different
>> feature sets. For this purpose, I followed the tutorial available at [1].
>>
>> Following is the code that I could develop so far.
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> *y = df1.indexx = preprocessing.scale(df1)phy_features = ['A', 'B',
>> 'C']phy_transformer = Pipeline(steps=[('imputer',
>> SimpleImputer(strategy='median')), ('scaler',
>> StandardScaler())])phy_processer = ColumnTransformer(transformers=[('phy',
>> phy_transformer, phy_features)])fa_features = ['D', 'E', 'F']fa_transformer
>> = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler',
>> StandardScaler())])fa_processer = ColumnTransformer(transformers=[('fa',
>> fa_transformer, fa_features)])pipe_phy = Pipeline(steps=[('preprocessor',
>> phy_processer ),('classifier', SVM)])pipe_fa =
>> Pipeline(steps=[('preprocessor', fa_processer ),('classifier', SVM)])ens =
>> VotingClassifier(estimators=[pipe_phy, pipe_fa])cv = KFold(n_splits=10,
>> random_state=None, shuffle=True)for train_index, test_index in
>> cv.split(x):    x_train, x_test = x[train_index], x[test_index]    y_train,
>> y_test = y[train_index], y[test_index]    ens.fit(x_train,y_train)
>> print(ens.score(x_test, y_test))*
>>
>> However, when running the code, I am getting an error saying *TypeError:
>> argument of type 'ColumnTransformer' is not iterable*, at the line
>> *ens.fit(x_train,y_train).*
>>
>> What is the reason for this and how can I fix it?
>>
>> Thank you,
>> Chamila
>>
>
>
> --
> Chamila Dilshan Wijayarathna,
> PhD Research Student
> The University of New South Wales (UNSW Canberra)
> Australian Centre for Cyber Security
> Australian Defence Force Academy
> PO Box 7916, Canberra BA ACT 2610
> Australia
> Mobile:(+61)416895795
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Chamila Dilshan Wijayarathna,
PhD Research Student
The University of New South Wales (UNSW Canberra)
Australian Centre for Cyber Security
Australian Defence Force Academy
PO Box 7916, Canberra BA ACT 2610
Australia
Mobile:(+61)416895795
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/9bc2cab8/attachment-0001.html>

From thomasjpfan at gmail.com  Fri May 29 11:52:03 2020
From: thomasjpfan at gmail.com (Thomas J Fan)
Date: Fri, 29 May 2020 11:52:03 -0400
Subject: [scikit-learn] sklearn Pipeline: argument of type
 'ColumnTransformer' is not iterable
In-Reply-To: <CAAGV2DjhVE6cCSpPyxsQPDp3+V2-_UkE+GBhrnmbvvrGZeDxPQ@mail.gmail.com>
References: <CAAGV2Dgw5K4zzV1F0TXo47eUUdRnLgsBWtYsW5Cqr3KoSkZo3w@mail.gmail.com>
 <CAAGV2DjywTF1pPHmutei8t75K0chpMv=kgpR2LDsWvw83hE1MA@mail.gmail.com>
 <52354c90-c217-42e4-86d7-77d1eb8c30a9@Canary>
 <CAAGV2DjhVE6cCSpPyxsQPDp3+V2-_UkE+GBhrnmbvvrGZeDxPQ@mail.gmail.com>
Message-ID: <bb8200eb-da82-41dc-b8a5-e8f7e7cf1abe@Canary>

Once

x = preprocessing.scale(df1)

is called, the input to your estimator is no longer a dataframe, so the column transformer can not use strings to select columns.

Thomas

> On Friday, May 29, 2020 at 11:46 AM, Chamila Wijayarathna <cdwijayarathna at gmail.com (mailto:cdwijayarathna at gmail.com)> wrote:
> Hi,
>
> Thanks, this solution fixed the issue. However, it introduces a new error, which was not there before.
>
> Traceback (most recent call last):
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\__init__.py", line 425, in _get_column_indices
> all_columns = X.columns
> AttributeError: 'numpy.ndarray' object has no attribute 'columns'
> During handling of the above exception, another exception occurred:
> Traceback (most recent call last):
> File "<input>", line 1, in <module>
> File "C:\Program Files\JetBrains\PyCharm 2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
> pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
> File "C:\Program Files\JetBrains\PyCharm 2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
> exec(compile(contents+"\n", file, 'exec'), glob, loc)
> File "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line 127, in <module>
> ens.fit(x_train,y_train)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py", line 265, in fit
> return super().fit(X, transformed_y, sample_weight)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py", line 81, in fit
> for idx, clf in enumerate(clfs) if clf not in (None, 'drop')
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py", line 1029, in __call__
> if self.dispatch_one_batch(iterator):
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py", line 847, in dispatch_one_batch
> self._dispatch(tasks)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py", line 765, in _dispatch
> job = self._backend.apply_async(batch, callback=cb)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\_parallel_backends.py", line 206, in apply_async
> result = ImmediateResult(func)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\_parallel_backends.py", line 570, in __init__
> self.results = batch()
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py", line 253, in __call__
> for func, args, kwargs in self.items]
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py", line 253, in <listcomp>
> for func, args, kwargs in self.items]
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py", line 40, in _fit_single_estimator
> estimator.fit(X, y)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py", line 330, in fit
> Xt = self._fit(X, y, **fit_params_steps)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py", line 296, in _fit
> **fit_params_steps[name])
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\memory.py", line 352, in __call__
> return self.func(*args, **kwargs)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py", line 740, in _fit_transform_one
> res = transformer.fit_transform(X, y, **fit_params)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\compose\_column_transformer.py", line 529, in fit_transform
> self._validate_remainder(X)
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\compose\_column_transformer.py", line 327, in _validate_remainder
> cols.extend(_get_column_indices(X, columns))
> File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\__init__.py", line 427, in _get_column_indices
> raise ValueError("Specifying the columns using strings is only "
> ValueError: Specifying the columns using strings is only supported for pandas DataFrames
>
> Thanks
> On Fri, May 29, 2020 at 7:33 PM Thomas J Fan <thomasjpfan at gmail.com (mailto:thomasjpfan at gmail.com)> wrote:
> > VotingClassifer also needs names:
> >
> > ens = VotingClassifier(estimators=[('pipe1', pipe_phy), ('pipe2', pipe_fa)])
> >
> > Thomas
> >
> > > On Friday, May 29, 2020 at 2:33 AM, Chamila Wijayarathna <cdwijayarathna at gmail.com (mailto:cdwijayarathna at gmail.com)> wrote:
> > > Hi all,
> > >
> > > I did manage to get the code to run using a workaround, which is bit ugly.
> > >
> > > Following is the complete stacktrace of the error I was receiving.
> > >
> > > Traceback (most recent call last):
> > > File "<input>", line 1, in <module>
> > > File "C:\Program Files\JetBrains\PyCharm 2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
> > > pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
> > > File "C:\Program Files\JetBrains\PyCharm 2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
> > > exec(compile(contents+"\n", file, 'exec'), glob, loc)
> > > File "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line 112, in <module>
> > > ens.fit(x_train,y_train)
> > > File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py", line 265, in fit
> > > return super().fit(X, transformed_y, sample_weight)
> > > File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py", line 65, in fit
> > > names, clfs = self._validate_estimators()
> > > File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py", line 228, in _validate_estimators
> > > self._validate_names(names)
> > > File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py", line 77, in _validate_names
> > > invalid_names = [name for name in names if '__' in name]
> > > File "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py", line 77, in <listcomp>
> > > invalid_names = [name for name in names if '__' in name]
> > > TypeError: argument of type 'ColumnTransformer' is not iterable
> > >
> > > Following are the inputs in 'names' list at the time of the error.
> > >
> > > 1- ColumnTransformer(transformers=[('phy', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), ['HR', 'RMSSD', 'SCL'])])
> > > 2- ColumnTransformer(transformers=[('fa',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler())]),['Squality', 'Sneutral', 'Shappy'])])
> > >
> > > Seems like that the library is attempting to search for '__' substring of the ColumnTransform object, which it is unable to perform.
> > >
> > > Since this name check doesn't have a signiticant effect on my functionality, I commented following snippet at sklearn\utils\metaestimators.py.
> > >
> > > invalid_names = [name for name in names if '__' in name]
> > > if invalid_names:
> > > raise ValueError('Estimator names must not contain __: got '
> > > '{0!r}'.format(invalid_names))
> > >
> > > Please let me know if there is a better workaround or that their are any issues of commenting out this code.
> > >
> > > Thanks
> > > On Fri, May 29, 2020 at 10:33 AM Chamila Wijayarathna <cdwijayarathna at gmail.com (mailto:cdwijayarathna at gmail.com)> wrote:
> > > > Hello all,
> > > >
> > > > I hope I am writing to the correct mailing list about this issue that I am having. Please apologize me if I am not.
> > > >
> > > > I am attempting to use a pipeline to feed an ensemble voting classifier as I want the ensemble learner to use models that train on different feature sets. For this purpose, I followed the tutorial available at [1].
> > > >
> > > > Following is the code that I could develop so far.
> > > >
> > > > y = df1.index
> > > > x = preprocessing.scale(df1)
> > > >
> > > > phy_features = ['A', 'B', 'C']
> > > > phy_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])
> > > > phy_processer = ColumnTransformer(transformers=[('phy', phy_transformer, phy_features)])
> > > >
> > > > fa_features = ['D', 'E', 'F']
> > > > fa_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])
> > > > fa_processer = ColumnTransformer(transformers=[('fa', fa_transformer, fa_features)])
> > > >
> > > >
> > > > pipe_phy = Pipeline(steps=[('preprocessor', phy_processer ),('classifier', SVM)])
> > > > pipe_fa = Pipeline(steps=[('preprocessor', fa_processer ),('classifier', SVM)])
> > > >
> > > > ens = VotingClassifier(estimators=[pipe_phy, pipe_fa])
> > > >
> > > > cv = KFold(n_splits=10, random_state=None, shuffle=True)
> > > > for train_index, test_index in cv.split(x):
> > > > x_train, x_test = x[train_index], x[test_index]
> > > > y_train, y_test = y[train_index], y[test_index]
> > > > ens.fit(x_train,y_train)
> > > > print(ens.score(x_test, y_test))
> > > >
> > > > However, when running the code, I am getting an error saying TypeError: argument of type 'ColumnTransformer' is not iterable, at the line ens.fit(x_train,y_train).
> > > >
> > > > What is the reason for this and how can I fix it?
> > > >
> > > > Thank you,
> > > > Chamila
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > >
> > >
> > >
> > >
> > > --
> > > Chamila Dilshan Wijayarathna,
> > > PhD Research Student
> > > The University of New South Wales (UNSW Canberra)
> > > Australian Centre for Cyber Security
> > > Australian Defence Force Academy
> > > PO Box 7916, Canberra BA ACT 2610
> > > Australia
> > > Mobile:(+61)416895795
> > >
> > >
> > > _______________________________________________
> > > scikit-learn mailing list
> > > scikit-learn at python.org (mailto:scikit-learn at python.org)
> > > https://mail.python.org/mailman/listinfo/scikit-learn
> > _______________________________________________
> > scikit-learn mailing list
> > scikit-learn at python.org (mailto:scikit-learn at python.org)
> > https://mail.python.org/mailman/listinfo/scikit-learn
>
>
> --
> Chamila Dilshan Wijayarathna,
> PhD Research Student
> The University of New South Wales (UNSW Canberra)
> Australian Centre for Cyber Security
> Australian Defence Force Academy
> PO Box 7916, Canberra BA ACT 2610
> Australia
> Mobile:(+61)416895795
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/17f2885d/attachment-0001.html>

From niourf at gmail.com  Fri May 29 12:25:58 2020
From: niourf at gmail.com (Nicolas Hug)
Date: Fri, 29 May 2020 12:25:58 -0400
Subject: [scikit-learn] sklearn Pipeline: argument of type
 'ColumnTransformer' is not iterable
In-Reply-To: <bb8200eb-da82-41dc-b8a5-e8f7e7cf1abe@Canary>
References: <CAAGV2Dgw5K4zzV1F0TXo47eUUdRnLgsBWtYsW5Cqr3KoSkZo3w@mail.gmail.com>
 <CAAGV2DjywTF1pPHmutei8t75K0chpMv=kgpR2LDsWvw83hE1MA@mail.gmail.com>
 <52354c90-c217-42e4-86d7-77d1eb8c30a9@Canary>
 <CAAGV2DjhVE6cCSpPyxsQPDp3+V2-_UkE+GBhrnmbvvrGZeDxPQ@mail.gmail.com>
 <bb8200eb-da82-41dc-b8a5-e8f7e7cf1abe@Canary>
Message-ID: <ddab168c-b496-2f2d-2294-af9956d39231@gmail.com>

Also, you should not scale your input before computing cross-validation 
scores. By doing that you are biasing your results because each test set 
knows something about the rest of the data (even if it's not target data)

The scaling should be applied independently on each (train / test) pair.

This can be done through a pipeline: 
https://scikit-learn.org/stable/modules/compose.html


On 5/29/20 11:52 AM, Thomas J Fan wrote:
> Once
>
> /x = preprocessing.scale(df1)/
> /
> /
> is called, the input to your estimator is no longer a dataframe, so 
> the column transformer can not use strings to select columns.
>
> Thomas
>
>     On Friday, May 29, 2020 at 11:46 AM, Chamila Wijayarathna
>     <cdwijayarathna at gmail.com <mailto:cdwijayarathna at gmail.com>> wrote:
>     Hi,
>
>     Thanks, this solution fixed the issue. However, it introduces a
>     new error, which was not there before.
>
>     Traceback (most recent call last):
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\__init__.py",
>     line 425, in _get_column_indices
>     ? ? all_columns = X.columns
>     AttributeError: 'numpy.ndarray' object has no attribute 'columns'
>     During handling of the above exception, another exception occurred:
>     Traceback (most recent call last):
>     ? File "<input>", line 1, in <module>
>     ? File "C:\Program Files\JetBrains\PyCharm
>     2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py",
>     line 197, in runfile
>     ? ? pydev_imports.execfile(filename, global_vars, local_vars) ?#
>     execute the script
>     ? File "C:\Program Files\JetBrains\PyCharm
>     2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py",
>     line 18, in execfile
>     ? ? exec(compile(contents+"\n", file, 'exec'), glob, loc)
>     ? File "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line
>     127, in <module>
>     ? ? ens.fit(x_train,y_train)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
>     line 265, in fit
>     ? ? return super().fit(X, transformed_y, sample_weight)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
>     line 81, in fit
>     ? ? for idx, clf in enumerate(clfs) if clf not in (None, 'drop')
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
>     line 1029, in __call__
>     ? ? if self.dispatch_one_batch(iterator):
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
>     line 847, in dispatch_one_batch
>     ? ? self._dispatch(tasks)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
>     line 765, in _dispatch
>     ? ? job = self._backend.apply_async(batch, callback=cb)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\_parallel_backends.py",
>     line 206, in apply_async
>     ? ? result = ImmediateResult(func)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\_parallel_backends.py",
>     line 570, in __init__
>     ? ? self.results = batch()
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
>     line 253, in __call__
>     ? ? for func, args, kwargs in self.items]
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
>     line 253, in <listcomp>
>     ? ? for func, args, kwargs in self.items]
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py",
>     line 40, in _fit_single_estimator
>     ? ? estimator.fit(X, y)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py",
>     line 330, in fit
>     ? ? Xt = self._fit(X, y, **fit_params_steps)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py",
>     line 296, in _fit
>     ? ? **fit_params_steps[name])
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\memory.py",
>     line 352, in __call__
>     ? ? return self.func(*args, **kwargs)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py",
>     line 740, in _fit_transform_one
>     ? ? res = transformer.fit_transform(X, y, **fit_params)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\compose\_column_transformer.py",
>     line 529, in fit_transform
>     ? ? self._validate_remainder(X)
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\compose\_column_transformer.py",
>     line 327, in _validate_remainder
>     ? ? cols.extend(_get_column_indices(X, columns))
>     ? File
>     "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\__init__.py",
>     line 427, in _get_column_indices
>     ? ? raise ValueError("Specifying the columns using strings is only "
>     ValueError: Specifying the columns using strings is only supported
>     for pandas DataFrames
>
>     Thanks
>
>     On Fri, May 29, 2020 at 7:33 PM Thomas J Fan
>     <thomasjpfan at gmail.com <mailto:thomasjpfan at gmail.com>> wrote:
>
>         VotingClassifer also needs names:
>
>         ens = VotingClassifier(estimators=[('pipe1', pipe_phy),
>         ('pipe2', pipe_fa)])
>
>         Thomas
>
>             On Friday, May 29, 2020 at 2:33 AM, Chamila Wijayarathna
>             <cdwijayarathna at gmail.com
>             <mailto:cdwijayarathna at gmail.com>> wrote:
>             Hi all,
>
>             I did manage to get the code to run using a workaround,
>             which is bit ugly.
>
>             Following is the complete stacktrace of the error I was
>             receiving.
>
>             /Traceback (most recent call last):
>             ? File "<input>", line 1, in <module>
>             ? File "C:\Program Files\JetBrains\PyCharm
>             2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py",
>             line 197, in runfile
>             ? ? pydev_imports.execfile(filename, global_vars,
>             local_vars) ?# execute the script
>             ? File "C:\Program Files\JetBrains\PyCharm
>             2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py",
>             line 18, in execfile
>             ? ? exec(compile(contents+"\n", file, 'exec'), glob, loc)
>             ? File
>             "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line
>             112, in <module>
>             ? ? ens.fit(x_train,y_train)
>             ? File
>             "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
>             line 265, in fit
>             ? ? return super().fit(X, transformed_y, sample_weight)
>             ? File
>             "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
>             line 65, in fit
>             ? ? names, clfs = self._validate_estimators()
>             ? File
>             "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py",
>             line 228, in _validate_estimators
>             ? ? self._validate_names(names)
>             ? File
>             "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py",
>             line 77, in _validate_names
>             ? ? invalid_names = [name for name in names if '__' in name]
>             ? File
>             "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py",
>             line 77, in <listcomp>
>             ? ? invalid_names = [name for name in names if '__' in name]
>             TypeError: argument of type 'ColumnTransformer' is not
>             iterable/
>             /
>             /
>             Following are the inputs in 'names' list at the time of
>             the error.
>
>             1- /ColumnTransformer(transformers=[('phy',
>             Pipeline(steps=[('imputer',
>             SimpleImputer(strategy='median')), ('scaler',
>             StandardScaler())]),?['HR', 'RMSSD', 'SCL'])])
>             2-
>             ColumnTransformer(transformers=[('fa',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',
>             StandardScaler())]),['Squality', 'Sneutral', 'Shappy'])])/
>
>             Seems like that the library is attempting to search for
>             '__' substring of the ColumnTransform object, which it is
>             unable to perform.
>
>             Since this name check doesn't have a signiticant effect on
>             my functionality, I commented following snippet at
>             /sklearn\utils\metaestimators.py./
>
>             /invalid_names = [name for name in names if '__' in name]
>             if invalid_names:
>             ? ? raise ValueError('Estimator names must not contain __:
>             got '
>             '{0!r}'.format(invalid_names))/
>
>             Please let me know if there is a better workaround or that
>             their are any issues of commenting out this code.
>
>             Thanks
>
>             On Fri, May 29, 2020 at 10:33 AM Chamila Wijayarathna
>             <cdwijayarathna at gmail.com
>             <mailto:cdwijayarathna at gmail.com>> wrote:
>
>                 Hello all,
>
>                 I hope I am writing to the correct mailing list about
>                 this issue that I am having. Please apologize me if I
>                 am not.
>
>                 I am attempting to use a pipeline to feed an ensemble
>                 voting classifier as I want the ensemble learner to
>                 use models that train on different feature sets. For
>                 this purpose, I followed the tutorial available at [1].
>
>                 Following is the code that I could develop so far.
>
>                 /y = df1.index
>                 x = preprocessing.scale(df1)
>
>                 phy_features = ['A', 'B', 'C']
>                 phy_transformer = Pipeline(steps=[('imputer',
>                 SimpleImputer(strategy='median')), ('scaler',
>                 StandardScaler())])
>                 phy_processer =
>                 ColumnTransformer(transformers=[('phy',
>                 phy_transformer, phy_features)])
>
>                 fa_features = ['D', 'E', 'F']
>                 fa_transformer = Pipeline(steps=[('imputer',
>                 SimpleImputer(strategy='median')), ('scaler',
>                 StandardScaler())])
>                 fa_processer = ColumnTransformer(transformers=[('fa',
>                 fa_transformer, fa_features)])
>
>
>                 pipe_phy = Pipeline(steps=[('preprocessor',
>                 phy_processer ),('classifier', SVM)])
>                 pipe_fa = Pipeline(steps=[('preprocessor',
>                 fa_processer ),('classifier', SVM)])
>
>                 ens = VotingClassifier(estimators=[pipe_phy, pipe_fa])
>
>                 cv = KFold(n_splits=10, random_state=None, shuffle=True)
>                 for train_index, test_index in cv.split(x):
>                 ? ? x_train, x_test = x[train_index], x[test_index]
>                 ? ? y_train, y_test = y[train_index], y[test_index]
>                 ? ? ens.fit(x_train,y_train)
>                 ? ? print(ens.score(x_test, y_test))/
>                 /
>                 /However, when running the code, I am getting an error
>                 saying /TypeError: argument of type
>                 'ColumnTransformer' is not iterable/, at the line
>                 /ens.fit(x_train,y_train)./
>
>                 What is the reason for this and how can I fix it?
>
>                 Thank you,
>                 Chamila
>
>
>
>             -- 
>             Chamila Dilshan Wijayarathna,
>             PhD Research Student
>             The University of New South Wales (UNSW Canberra)
>             Australian Centre for Cyber Security
>             Australian Defence Force Academy
>             PO Box 7916, Canberra BA ACT 2610
>             Australia
>             Mobile:(+61)416895795
>
>             _______________________________________________
>             scikit-learn mailing list
>             scikit-learn at python.org <mailto:scikit-learn at python.org>
>             https://mail.python.org/mailman/listinfo/scikit-learn
>
>         _______________________________________________
>         scikit-learn mailing list
>         scikit-learn at python.org <mailto:scikit-learn at python.org>
>         https://mail.python.org/mailman/listinfo/scikit-learn
>
>
>
>     -- 
>     Chamila Dilshan Wijayarathna,
>     PhD Research Student
>     The University of New South Wales (UNSW Canberra)
>     Australian Centre for Cyber Security
>     Australian Defence Force Academy
>     PO Box 7916, Canberra BA ACT 2610
>     Australia
>     Mobile:(+61)416895795
>
>     _______________________________________________
>     scikit-learn mailing list
>     scikit-learn at python.org
>     https://mail.python.org/mailman/listinfo/scikit-learn
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200529/86dc5e4f/attachment-0001.html>

From cdwijayarathna at gmail.com  Sat May 30 01:20:31 2020
From: cdwijayarathna at gmail.com (Chamila Wijayarathna)
Date: Sat, 30 May 2020 10:50:31 +0530
Subject: [scikit-learn] sklearn Pipeline: argument of type
 'ColumnTransformer' is not iterable
In-Reply-To: <ddab168c-b496-2f2d-2294-af9956d39231@gmail.com>
References: <CAAGV2Dgw5K4zzV1F0TXo47eUUdRnLgsBWtYsW5Cqr3KoSkZo3w@mail.gmail.com>
 <CAAGV2DjywTF1pPHmutei8t75K0chpMv=kgpR2LDsWvw83hE1MA@mail.gmail.com>
 <52354c90-c217-42e4-86d7-77d1eb8c30a9@Canary>
 <CAAGV2DjhVE6cCSpPyxsQPDp3+V2-_UkE+GBhrnmbvvrGZeDxPQ@mail.gmail.com>
 <bb8200eb-da82-41dc-b8a5-e8f7e7cf1abe@Canary>
 <ddab168c-b496-2f2d-2294-af9956d39231@gmail.com>
Message-ID: <CAAGV2Dgnx7xs45G65J8U-Erme2oFMFDsy9MTTMq0sT2-UyNnZQ@mail.gmail.com>

Thank you both for your inputs.

On Fri, May 29, 2020 at 9:57 PM Nicolas Hug <niourf at gmail.com> wrote:

> Also, you should not scale your input before computing cross-validation
> scores. By doing that you are biasing your results because each test set
> knows something about the rest of the data (even if it's not target data)
>
> The scaling should be applied independently on each (train / test) pair.
>
> This can be done through a pipeline:
> https://scikit-learn.org/stable/modules/compose.html
>
>
> On 5/29/20 11:52 AM, Thomas J Fan wrote:
>
> Once
>
> *x = preprocessing.scale(df1)*
>
> is called, the input to your estimator is no longer a dataframe, so the
> column transformer can not use strings to select columns.
>
> Thomas
>
> On Friday, May 29, 2020 at 11:46 AM, Chamila Wijayarathna <
> cdwijayarathna at gmail.com> wrote:
> Hi,
>
> Thanks, this solution fixed the issue. However, it introduces a new error,
> which was not there before.
>
> Traceback (most recent call last):
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\__init__.py",
> line 425, in _get_column_indices
>     all_columns = X.columns
> AttributeError: 'numpy.ndarray' object has no attribute 'columns'
> During handling of the above exception, another exception occurred:
> Traceback (most recent call last):
>   File "<input>", line 1, in <module>
>   File "C:\Program Files\JetBrains\PyCharm
> 2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line
> 197, in runfile
>     pydev_imports.execfile(filename, global_vars, local_vars)  # execute
> the script
>   File "C:\Program Files\JetBrains\PyCharm
> 2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line
> 18, in execfile
>     exec(compile(contents+"\n", file, 'exec'), glob, loc)
>   File "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line 127, in
> <module>
>     ens.fit(x_train,y_train)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
> line 265, in fit
>     return super().fit(X, transformed_y, sample_weight)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
> line 81, in fit
>     for idx, clf in enumerate(clfs) if clf not in (None, 'drop')
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
> line 1029, in __call__
>     if self.dispatch_one_batch(iterator):
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
> line 847, in dispatch_one_batch
>     self._dispatch(tasks)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
> line 765, in _dispatch
>     job = self._backend.apply_async(batch, callback=cb)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\_parallel_backends.py",
> line 206, in apply_async
>     result = ImmediateResult(func)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\_parallel_backends.py",
> line 570, in __init__
>     self.results = batch()
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
> line 253, in __call__
>     for func, args, kwargs in self.items]
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\parallel.py",
> line 253, in <listcomp>
>     for func, args, kwargs in self.items]
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py",
> line 40, in _fit_single_estimator
>     estimator.fit(X, y)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py",
> line 330, in fit
>     Xt = self._fit(X, y, **fit_params_steps)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py",
> line 296, in _fit
>     **fit_params_steps[name])
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\joblib\memory.py",
> line 352, in __call__
>     return self.func(*args, **kwargs)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\pipeline.py",
> line 740, in _fit_transform_one
>     res = transformer.fit_transform(X, y, **fit_params)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\compose\_column_transformer.py",
> line 529, in fit_transform
>     self._validate_remainder(X)
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\compose\_column_transformer.py",
> line 327, in _validate_remainder
>     cols.extend(_get_column_indices(X, columns))
>   File
> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\__init__.py",
> line 427, in _get_column_indices
>     raise ValueError("Specifying the columns using strings is only "
> ValueError: Specifying the columns using strings is only supported for
> pandas DataFrames
>
> Thanks
>
> On Fri, May 29, 2020 at 7:33 PM Thomas J Fan <thomasjpfan at gmail.com>
> wrote:
>
>> VotingClassifer also needs names:
>>
>> ens = VotingClassifier(estimators=[('pipe1', pipe_phy), ('pipe2',
>> pipe_fa)])
>>
>> Thomas
>>
>> On Friday, May 29, 2020 at 2:33 AM, Chamila Wijayarathna <
>> cdwijayarathna at gmail.com> wrote:
>> Hi all,
>>
>> I did manage to get the code to run using a workaround, which is bit ugly.
>>
>> Following is the complete stacktrace of the error I was receiving.
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> *Traceback (most recent call last):   File "<input>", line 1, in <module>
>>   File "C:\Program Files\JetBrains\PyCharm
>> 2020.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line
>> 197, in runfile     pydev_imports.execfile(filename, global_vars,
>> local_vars)  # execute the script   File "C:\Program
>> Files\JetBrains\PyCharm
>> 2020.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line
>> 18, in execfile     exec(compile(contents+"\n", file, 'exec'), glob, loc)
>> File "C:/Users/ASUS/PycharmProjects/swelltest/enemble.py", line 112, in
>> <module>     ens.fit(x_train,y_train)   File
>> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
>> line 265, in fit     return super().fit(X, transformed_y, sample_weight)
>> File
>> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_voting.py",
>> line 65, in fit     names, clfs = self._validate_estimators()   File
>> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\ensemble\_base.py",
>> line 228, in _validate_estimators     self._validate_names(names)   File
>> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py",
>> line 77, in _validate_names     invalid_names = [name for name in names if
>> '__' in name]   File
>> "C:\Users\ASUS\PycharmProjects\swelltest\venv\lib\site-packages\sklearn\utils\metaestimators.py",
>> line 77, in <listcomp>     invalid_names = [name for name in names if '__'
>> in name] TypeError: argument of type 'ColumnTransformer' is not iterable*
>>
>> Following are the inputs in 'names' list at the time of the error.
>>
>> 1-
>> *ColumnTransformer(transformers=[('phy', Pipeline(steps=[('imputer',
>> SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), ['HR',
>> 'RMSSD', 'SCL'])]) 2-
>> ColumnTransformer(transformers=[('fa',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',
>> StandardScaler())]),['Squality', 'Sneutral', 'Shappy'])])*
>>
>> Seems like that the library is attempting to search for '__' substring of
>> the ColumnTransform object, which it is unable to perform.
>>
>> Since this name check doesn't have a signiticant effect on my
>> functionality, I commented following snippet at
>> *sklearn\utils\metaestimators.py.*
>>
>>
>>
>>
>> *invalid_names = [name for name in names if '__' in name] if
>> invalid_names:     raise ValueError('Estimator names must not contain __:
>> got '                     '{0!r}'.format(invalid_names))*
>>
>> Please let me know if there is a better workaround or that their are any
>> issues of commenting out this code.
>>
>> Thanks
>>
>> On Fri, May 29, 2020 at 10:33 AM Chamila Wijayarathna <
>> cdwijayarathna at gmail.com> wrote:
>>
>>> Hello all,
>>>
>>> I hope I am writing to the correct mailing list about this issue that I
>>> am having. Please apologize me if I am not.
>>>
>>> I am attempting to use a pipeline to feed an ensemble voting classifier
>>> as I want the ensemble learner to use models that train on different
>>> feature sets. For this purpose, I followed the tutorial available at [1].
>>>
>>> Following is the code that I could develop so far.
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> *y = df1.index x = preprocessing.scale(df1) phy_features = ['A', 'B',
>>> 'C'] phy_transformer = Pipeline(steps=[('imputer',
>>> SimpleImputer(strategy='median')), ('scaler', StandardScaler())])
>>> phy_processer = ColumnTransformer(transformers=[('phy', phy_transformer,
>>> phy_features)]) fa_features = ['D', 'E', 'F'] fa_transformer =
>>> Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler',
>>> StandardScaler())]) fa_processer = ColumnTransformer(transformers=[('fa',
>>> fa_transformer, fa_features)]) pipe_phy = Pipeline(steps=[('preprocessor',
>>> phy_processer ),('classifier', SVM)]) pipe_fa =
>>> Pipeline(steps=[('preprocessor', fa_processer ),('classifier', SVM)]) ens =
>>> VotingClassifier(estimators=[pipe_phy, pipe_fa]) cv = KFold(n_splits=10,
>>> random_state=None, shuffle=True) for train_index, test_index in
>>> cv.split(x):     x_train, x_test = x[train_index], x[test_index]
>>> y_train, y_test = y[train_index], y[test_index]
>>> ens.fit(x_train,y_train)     print(ens.score(x_test, y_test))*
>>>
>>> However, when running the code, I am getting an error saying *TypeError:
>>> argument of type 'ColumnTransformer' is not iterable*, at the line
>>> *ens.fit(x_train,y_train).*
>>>
>>> What is the reason for this and how can I fix it?
>>>
>>> Thank you,
>>> Chamila
>>>
>>
>>
>> --
>> Chamila Dilshan Wijayarathna,
>> PhD Research Student
>> The University of New South Wales (UNSW Canberra)
>> Australian Centre for Cyber Security
>> Australian Defence Force Academy
>> PO Box 7916, Canberra BA ACT 2610
>> Australia
>> Mobile:(+61)416895795
>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>
>
> --
> Chamila Dilshan Wijayarathna,
> PhD Research Student
> The University of New South Wales (UNSW Canberra)
> Australian Centre for Cyber Security
> Australian Defence Force Academy
> PO Box 7916, Canberra BA ACT 2610
> Australia
> Mobile:(+61)416895795
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
> _______________________________________________
> scikit-learn mailing listscikit-learn at python.orghttps://mail.python.org/mailman/listinfo/scikit-learn
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Chamila Dilshan Wijayarathna,
PhD Research Student
The University of New South Wales (UNSW Canberra)
Australian Centre for Cyber Security
Australian Defence Force Academy
PO Box 7916, Canberra BA ACT 2610
Australia
Mobile:(+61)416895795
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200530/b33c1385/attachment-0001.html>

From pedro.cardoso.code at gmail.com  Sat May 30 14:34:40 2020
From: pedro.cardoso.code at gmail.com (Pedro Cardoso)
Date: Sat, 30 May 2020 19:34:40 +0100
Subject: [scikit-learn] [GridSearchCV] Reduction of elapsed time at the
 second interation
In-Reply-To: <CACDxx9i1276QWa47w8sHh3cGotAKqibkNfRO025w_LLOd+_83A@mail.gmail.com>
References: <CACSmxPJkxZBUmQvpJSAukWNX3fmbRk96TkWZwLCB6BASTXQmJg@mail.gmail.com>
 <CACDxx9i1276QWa47w8sHh3cGotAKqibkNfRO025w_LLOd+_83A@mail.gmail.com>
Message-ID: <CACSmxPLwv=5Tsi0WvXuf1sUGwdFx_eKNsdsEB2+UsjhBzmC=Tw@mail.gmail.com>

Hey Guillaume,

first of all, thank you for the help. I checked my code and memory is
turned of (parameter is using default). And yes, I am using a different
number of features  everytime.


Guillaume Lema?tre <g.lemaitre58 at gmail.com> escreveu no dia quarta,
27/05/2020 ?(s) 16:55:

> Regarding scikit-learn, the only thing that we cache is the transformer
> processing in the pipeline (see the memory parameter in Pipeline).
>
> It seems that you are passing a different set of features at each
> iteration. Is the number of features different?
>
> On Sun, 29 Mar 2020 at 19:23, Pedro Cardoso <pedro.cardoso.code at gmail.com>
> wrote:
>
>> Hello fellows,
>>
>> i am knew at slkearn and I have a question about GridSearchCV:
>>
>> I am running the following code  at a jupyter notebook :
>>
>> ----------------------*code*-------------------------------
>>
>> opt_models = dict()
>> for feature in [features1, features2, features3, features4]:
>>     cmb = CMB(x_train, y_train, x_test, y_test, feature)
>>     cmb.fit()
>>     cmb.predict()
>>     opt_models[str(feature)]=cmb.get_best_model()
>>
>> -------------------------------------------------------
>>
>> The CMB class is just a class that contains different classification
>> models (SVC, decision tree, etc...). When cmb.fit() is running, a
>> gridSearchCV is performed at the SVC model  (which is within the cmb
>> instance) in order to tune the hyperparameters C, gamma, and kernel. The
>> SCV model is implemented using the sklearn.svm.SVC class. Here is the
>> output of the first and second iteration of the for loop:
>>
>> ---------------------*output*-------------------------------------
>> -> 1st iteration
>>
>>
>> Fitting 5 folds for each of 12 candidates, totalling 60 fits
>>
>> [Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
>> [Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    6.1s
>> [Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    6.1s
>> [Parallel(n_jobs=-1)]: Done   3 tasks      | elapsed:    6.1s
>> [Parallel(n_jobs=-1)]: Done   4 tasks      | elapsed:    6.2s
>> [Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    6.2s
>> [Parallel(n_jobs=-1)]: Done   6 tasks      | elapsed:    6.2s
>> [Parallel(n_jobs=-1)]: Done   7 tasks      | elapsed:    6.2s
>> [Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    6.2s
>> [Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    6.2s
>> [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    6.2s
>> [Parallel(n_jobs=-1)]: Done  11 tasks      | elapsed:    6.2s
>> [Parallel(n_jobs=-1)]: Done  12 tasks      | elapsed:    6.3s
>> [Parallel(n_jobs=-1)]: Done  13 tasks      | elapsed:    6.3s
>> [Parallel(n_jobs=-1)]: Done  14 tasks      | elapsed:    6.3s
>> [Parallel(n_jobs=-1)]: Done  15 tasks      | elapsed:    6.4s
>> [Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    6.4s
>> [Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    6.4s
>> [Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    6.4s
>> [Parallel(n_jobs=-1)]: Done  19 tasks      | elapsed:    6.5s
>> [Parallel(n_jobs=-1)]: Done  20 tasks      | elapsed:    6.5s
>> [Parallel(n_jobs=-1)]: Done  21 tasks      | elapsed:    6.5s
>> [Parallel(n_jobs=-1)]: Done  22 tasks      | elapsed:    6.6s
>> [Parallel(n_jobs=-1)]: Done  23 tasks      | elapsed:    6.7s
>> [Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    6.7s
>> [Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    6.7s
>> [Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    6.8s
>> [Parallel(n_jobs=-1)]: Done  27 tasks      | elapsed:    6.8s
>> [Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    6.9s
>> [Parallel(n_jobs=-1)]: Done  29 tasks      | elapsed:    6.9s
>> [Parallel(n_jobs=-1)]: Done  30 tasks      | elapsed:    6.9s
>> [Parallel(n_jobs=-1)]: Done  31 tasks      | elapsed:    7.0s
>> [Parallel(n_jobs=-1)]: Done  32 tasks      | elapsed:    7.0s
>> [Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    7.0s
>> [Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    7.0s
>> [Parallel(n_jobs=-1)]: Done  35 tasks      | elapsed:    7.1s
>> [Parallel(n_jobs=-1)]: Done  36 tasks      | elapsed:    7.1s
>> [Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:    7.2s
>> [Parallel(n_jobs=-1)]: Done  38 tasks      | elapsed:    7.2s
>> [Parallel(n_jobs=-1)]: Done  39 tasks      | elapsed:    7.2s
>> [Parallel(n_jobs=-1)]: Done  40 tasks      | elapsed:    7.2s
>> [Parallel(n_jobs=-1)]: Done  41 tasks      | elapsed:    7.3s
>> [Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    7.3s
>> [Parallel(n_jobs=-1)]: Done  43 tasks      | elapsed:    7.3s
>> [Parallel(n_jobs=-1)]: Done  44 tasks      | elapsed:    7.4s
>> [Parallel(n_jobs=-1)]: Done  45 tasks      | elapsed:    7.4s
>> [Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    7.5s
>>
>>
>> -> 2nd iteration
>>
>> Fitting 5 folds for each of 12 candidates, totalling 60 fits
>>
>> [Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
>> [Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.0s
>> [Parallel(n_jobs=-1)]: Batch computation too fast (0.0260s.) Setting batch_size=14.
>> [Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s
>> [Parallel(n_jobs=-1)]: Done   3 tasks      | elapsed:    0.0s
>> [Parallel(n_jobs=-1)]: Done   4 tasks      | elapsed:    0.0s
>> [Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    0.0s
>> [Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:    0.7s finished
>>
>>
>> ---------------------------------------------------------------------------------------------------------------------
>>
>>
>> As you can see, the first iteration gets a elapsed time much larger than
>> the 2nd iteration. Does it make sense? I am afraid that the model  is doing
>> some kind of cache or shortcut from the 1st iteration, and consequently
>> could decrease the model training/performance? I already read the sklearn
>> documentation and I didn't saw any warning/note about this kind of
>> behaviour.
>>
>> Thank you very much for your time :)
>>
>>
>>
>>
>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>
>
> --
> Guillaume Lemaitre
> Scikit-learn @ Inria Foundation
> https://glemaitre.github.io/
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20200530/88562592/attachment.html>