From kmaroank at gmail.com Mon Jul 11 01:19:09 2022
From: kmaroank at gmail.com (Maroan K)
Date: Mon, 11 Jul 2022 08:19:09 +0300
Subject: [scikitlearn] Double hinge loss
MessageID:
Hi,
Could anyone please give the derivative of the double hinge loss (DH)?
DH = max(wx,max(0, 1/2  1/2 * wx))
I guess the derivative of DH is usually wx , is this right?
Sorry if this is not the right place to ask such a question.
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From alan.isaac at gmail.com Mon Jul 11 08:42:09 2022
From: alan.isaac at gmail.com (Alan G. Isaac)
Date: Mon, 11 Jul 2022 08:42:09 0400
Subject: [scikitlearn] Double hinge loss
InReplyTo:
References:
MessageID:
On 7/11/2022 1:19 AM, Maroan K wrote:
>
> Could anyone please give the derivative?of the double hinge loss (DH)?
>
> DH = max(wx,max(0, 1/2  1/2 * wx))
>
> I guess the derivative of DH is usually wx , is this right?
>
> Sorry if this is not the right place to ask such a question.
1. The right place for such a question is e.g. https://math.stackexchange.com/
2. Since it is hinged, it is not everywhere differentiable.
Alan Isaac
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From thomasjpfan at gmail.com Sun Jul 17 13:12:05 2022
From: thomasjpfan at gmail.com (Thomas J. Fan)
Date: Sun, 17 Jul 2022 12:12:05 0500
Subject: [scikitlearn] VOTE SLEP018  Pandas Output for Transformers
MessageID:
Hi everyone,
SLEP018
introduces a set_output API for outputting pandas DataFrames in
transformers. An implementation for this SLEP is available in PR #23734
. Feedback is
welcome!
If you are a scikitlearn core developer please cast your vote in this PR
. According
to our governance model
,
the vote will be open for a month (till 17th August), and the motion is
accepted if 2/3 of the cast votes are in favor.
Best,
Thomas
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From thomasjpfan at gmail.com Mon Jul 18 22:52:05 2022
From: thomasjpfan at gmail.com (Thomas J. Fan)
Date: Mon, 18 Jul 2022 22:52:05 0400
Subject: [scikitlearn] scikitlearn monthly developer meeting: Monday July
25, 2022
MessageID:
Dear all,
The scikitlearn developer monthly meeting will take place on Monday
July 25, 2022 at 15:00 UTC.
 Video call link: https://meet.google.com/ewsuszudjs
 Meeting notes / agenda: https://hackmd.io/0yokz72CTZSny8y3Re648Q
 Local times:
https://www.timeanddate.com/worldclock/meetingdetails.html?year=2022&month=7&day=25&hour=15&min=0&sec=0&p1=1440&p2=240&p3=248&p4=195&p5=179&p6=224
The goal of this meeting is to discuss ongoing development topics for
the project. Everybody is welcome.
As usual, please follow the code of conduct of the project:
https://github.com/scikitlearn/scikitlearn/blob/main/CODE_OF_CONDUCT.md
Regards,
Thomas
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From krallinger.martin at gmail.com Wed Jul 20 07:23:18 2022
From: krallinger.martin at gmail.com (Martin Krallinger)
Date: Wed, 20 Jul 2022 13:23:18 +0200
Subject: [scikitlearn] Call for Participation ClinSpEn at Biomedical WMT
Shared Task (WMT/EMNLP 2022
MessageID:
Call for Participation ClinSpEn @ Biomedical WMT Shared Task (WMT/EMNLP
2022)
Automatic Translation of Clinical cases, ontologies & medical entities:
Spanish  English
https://temu.bsc.es/clinspen/
ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to
promote the development and evaluation of machine translation systems
adapted to the medical domain with three highly relevant subtracks:
clinical cases, medical controlled vocabularies/ontologies, and clinical
terms and entities extracted from medical content.
Key information:

ClinSpEn subtrack: https://temu.bsc.es/clinspen/

Biomedical WMT: https://statmt.org/wmt22/biomedicaltranslationtask.html

Main WMT: https://statmt.org/wmt22/

EMNLP conference: https://2022.emnlp.org/

Sample/Training Data:

Clinical Cases: https://doi.org/10.5281/zenodo.6497350

Clinical Terms: https://doi.org/10.5281/zenodo.6497372

Ontology Concepts: https://doi.org/10.5281/zenodo.6497388

BioWMT Registration Form: https://tinyurl.com/mtvdytmt

ClinSpEn Registration Form (for support and updates):
https://temu.bsc.es/clinspen/registration/
Motivation
Machine translation applied to the clinical domain is a specially
challenging task due to the complexity of medical language and the heavy
use of healthrelated technical terms and medical expressions. Therefore,
there is a large community of specialized medical translators, able to deal
with medical narratives, terminologies or the use of ambiguous
abbreviations and acronyms.
Taking into account the relevance, impact and diversity of healthrelated
content, as well as the rapidly growing number of publications, EHRs,
clinical trials, informed consent documents and medical terminologies there
is a pressing need to be able to generate more robust medical machine
translation resources together with independent quality evaluation
scenarios.
Recent advances in machine translation technologies, together with the use
of other NLP components, are showing promising results, thus domain
adaptation of MT approaches can have a significant impact in unlocking key
information from medical content.
The ClinSpEn subtask of Biomedical WMT proposes three different highly
relevant subtracks, each associated with highly relevant medical machine
translation application scenarios::

ClinSpEnCC (Clinical Cases) subtask: translation of clinical case
documents from English to Spanish, a type of document relevant both for
processing medical literature as well as clinical records.

ClinSpEnCT (Clinical Terms): translation of clinical terms and entity
mentions from Spanish to English. The used terms were directly extracted
from medical literature and clinical records, with particular focus on
diseases, symptoms, findings, procedures and professions.

ClinSpEnOC (Ontology Concepts): translation of clinical controlled
vocabularies and ontology concepts from English to Spanish. Ontologies and
structured vocabularies represent a key resource for semantic
interoperability, entity linking, biomedical knowledge bases and precision
medicine, and thus there is a pressing need to generate multilingual
biomedical ontologies for a range of clinical applications.
A decentlysized sample set for each data type has been released.
Participants may use it to test their existing systems or try out new ones.
In addition to the manually translated test set by professional medical
translators, participants will also have access to a larger background
collection for each of the three substracks, which might serve as
additional resources and to promote scalability and robustness assessment
of machine translation technology.
Schedule

Test and Background Set Release: July 21st, 2022

Participant Predictions Due: July 28th, 2022

Paper Submission Deadline: September 7th, 2022

Notification of Acceptance (peerreviews): October 9th, 2022

Cameraready Version Due: October 16th, 2022

WMT @ EMNLP: December 7th and 8th, 2022
[All deadlines are in AoE (Anywhere on Earth)]
Registration
Participants must register using the official BioWMT Registration Form,
which is available at https://tinyurl.com/mtvdytmt.
Additionally, we?ve created a registration form specific for the ClinSpEn
subtracks which will be used to keep participants updated. Register at:
https://temu.bsc.es/clinspen/registration/.
Publications and WMT workshop
Teams participating in the ClinSpEn subtrack of Biomedical WMT will be
invited to contribute a systems description paper for the WMT 2022 Working
Notes proceedings. More information on the paper?s specifications,
formatting guidelines and review process at:
https://statmt.org/wmt22/index.html.
If you are interested in Machine Translation, the biomedical domain or
other language combinations, remember to check out the Biomedical WMT site
and the rest of this year?s subtracks and language pairs:
https://statmt.org/wmt22/biomedicaltranslationtask.html
ClinSpEn Organizers

Salvador LimaL?pez (Barcelona Supercomputing Center, Spain)

Darryl Johan Estrada (Barcelona Supercomputing Center, Spain)

Eul?lia Farr?Maduell (Barcelona Supercomputing Center, Spain)

Martin Krallinger (Barcelona Supercomputing Center, Spain)
Biomedical WMT Organizers

Rachel Bawden (University of Edinburgh, UK)

Giorgio Maria Di Nunzio (University of Padua, Italy)

Darryl Johan Estrada (Barcelona Supercomputing Center, Spain)

Eul?lia Farr?Maduell (Barcelona Supercomputing Center, Spain)

Cristian Grozea (Fraunhofer Institute, Germany)

Antonio Jimeno Yepes (University of Melbourne, Australia)

Salvador LimaL?pez (Barcelona Supercomputing Center, Spain)

Martin Krallinger (Barcelona Supercomputing Center, Spain)

Aur?lie N?v?ol (Universit? Paris Saclay, CNRS, LISN, France)

Mariana Neves (German Federal Institute for Risk Assessment, Germany)

Roland Roller (DFKI, Germany)

Amy Siu (Beuth University of Applied Sciences, Germany)

Philippe Thomas (DFKI, Germany)

Federica Vezzani (University of Padua, Italy)

Maika Vicente Navarro, Maika Spanish Translator, Melbourne, Australia

Dina Wiemann (Novartis, Switzerland)

Lana Yeganova (NCBI/NLM/NIH, USA
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From srhsieh at yahoo.com Fri Jul 29 17:27:29 2022
From: srhsieh at yahoo.com (ShangRou Hsieh)
Date: Fri, 29 Jul 2022 21:27:29 +0000 (UTC)
Subject: [scikitlearn] question regarding 'RANSACRegressor' object has no
attribute 'inlier_mask_'
References: <1719874980.2277622.1659130049290.ref@mail.yahoo.com>
MessageID: <1719874980.2277622.1659130049290@mail.yahoo.com>
To whom it may concern,
Belows are the codes:
?    
from sklearn.linear_model import RANSACRegressor
ransac = RANSACRegressor(LinearRegression(),
???????????????????????? max_trials=100, # default
???????????????????????? min_samples=0.95,
???????????????????????? loss='absolute_error', # default
???????????????????????? residual_threshold=None, # default
???????????????????????? random_state=123)
inlier_mask = ransac.inlier_mask_
   
Here is the error message:
AttributeError: 'RANSACRegressor' object has no attribute 'inlier_mask_'
SO I checked the attributes of RANSACRegressor using dir (RANSACRegressor) and I do not find 'inlier_mask_'
Any advise?Henry
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From g.lemaitre58 at gmail.com Fri Jul 29 18:06:17 2022
From: g.lemaitre58 at gmail.com (=?utf8?Q?Guillaume_Lema=C3=AEtre?=)
Date: Sat, 30 Jul 2022 00:06:17 +0200
Subject: [scikitlearn] question regarding 'RANSACRegressor' object has
no attribute 'inlier_mask_'
InReplyTo: <1719874980.2277622.1659130049290@mail.yahoo.com>
References: <1719874980.2277622.1659130049290.ref@mail.yahoo.com>
<1719874980.2277622.1659130049290@mail.yahoo.com>
MessageID: <12CB309584A74623A29BF2B87F532D3A@gmail.com>
You need to fit the estimator to access the fitted attribute:
In [1]: from sklearn.linear_model import RANSACRegressor
...: from sklearn.datasets import make_regression
...: X, y = make_regression(
...: n_samples=200, n_features=2, noise=4.0, random_state=0)
...: reg = RANSACRegressor(random_state=0).fit(X, y)
In [2]:
In [2]: reg.inlier_mask_
Out[2]:
array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True])
Cheers,

Guillaume Lemaitre
Scikitlearn @ Inria Foundation
https://glemaitre.github.io/
> On 29 Jul 2022, at 23:27, ShangRou Hsieh via scikitlearn wrote:
>
> To whom it may concern,
>
> Belows are the codes:
>
>     
> from sklearn.linear_model import RANSACRegressor
>
> ransac = RANSACRegressor(LinearRegression(),
> max_trials=100, # default
> min_samples=0.95,
> loss='absolute_error', # default
> residual_threshold=None, # default
> random_state=123)
>
> inlier_mask = ransac.inlier_mask_
>
>
>
>    
> Here is the error message:
>
> AttributeError: 'RANSACRegressor' object has no attribute 'inlier_mask_'
>
> SO I checked the attributes of RANSACRegressor using dir (RANSACRegressor) and I do not find 'inlier_mask_'
>
>
> Any advise?
> Henry
>
>
> _______________________________________________
> scikitlearn mailing list
> scikitlearn at python.org
> https://mail.python.org/mailman/listinfo/scikitlearn
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From srhsieh at yahoo.com Fri Jul 29 18:41:37 2022
From: srhsieh at yahoo.com (ShangRou Hsieh)
Date: Fri, 29 Jul 2022 22:41:37 +0000 (UTC)
Subject: [scikitlearn] question regarding 'RANSACRegressor' object has
no attribute 'inlier_mask_'
InReplyTo: <12CB309584A74623A29BF2B87F532D3A@gmail.com>
References: <1719874980.2277622.1659130049290.ref@mail.yahoo.com>
<1719874980.2277622.1659130049290@mail.yahoo.com>
<12CB309584A74623A29BF2B87F532D3A@gmail.com>
MessageID: <251038468.2288582.1659134497759@mail.yahoo.com>
Thanks. I will give it a try.
On Friday, July 29, 2022 at 03:06:19 PM PDT, Guillaume Lema?tre wrote:
You need to fit the estimator to access the fitted attribute:
In [1]: from sklearn.linear_model import RANSACRegressor?? ...: from sklearn.datasets import make_regression?? ...: X, y = make_regression(?? ...: ? ? n_samples=200, n_features=2, noise=4.0, random_state=0)?? ...: reg = RANSACRegressor(random_state=0).fit(X, y)
In [2]:?
In [2]: reg.inlier_mask_Out[2]:?array([ True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True])
Cheers,Guillaume Lemaitre
Scikitlearn @ Inria Foundation
https://glemaitre.github.io/
On 29 Jul 2022, at 23:27, ShangRou Hsieh via scikitlearn wrote:
To whom it may concern,
Belows are the codes:
?    
from sklearn.linear_model import RANSACRegressor
ransac = RANSACRegressor(LinearRegression(),
???????????????????????? max_trials=100, # default
???????????????????????? min_samples=0.95,
???????????????????????? loss='absolute_error', # default
???????????????????????? residual_threshold=None, # default
???????????????????????? random_state=123)
inlier_mask = ransac.inlier_mask_
   
Here is the error message:
AttributeError: 'RANSACRegressor' object has no attribute 'inlier_mask_'
SO I checked the attributes of RANSACRegressor using dir (RANSACRegressor) and I do not find 'inlier_mask_'
Any advise?Henry
_______________________________________________
scikitlearn mailing list
scikitlearn at python.org
https://mail.python.org/mailman/listinfo/scikitlearn
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