[scikit-learn] Fwd: [SP] Standardized system for parameter management across Scientific Python libraries

This sounds quite related to our parameter validation stuff. Thought I'd share. -- Forwarded message - From: Ewout ter Hoeven via Scientific Python < nore...@discuss.scientific-python.org> Date: Thu, Sep 12, 2024 at 9:57 AM Subject: Re: [SP] Standardized system for parameter manage

[scikit-learn] Fwd: [SciPy-Dev] Use of another CI provider for native linux_aarch64 and macosx_arm64

Just FYI. The difference in build times seem quite significant. -- Forwarded message - From: Andrew Nelson Date: Fri, Sep 16, 2022 at 12:37 PM Subject: [SciPy-Dev] Use of another CI provider for native linux_aarch64 and macosx_arm64 To: scipy-dev Hi all, I've been investigati

Re: [scikit-learn] Fwd: There is a problem with using "r2" to calculate cross_val_score and GridSearchCV scores

I am not surprised to observe these variations. First, the oob score tends to overestimate the statistical performance of the model. Then the last score is an evaluation without cross validation. Therefore, you trained a single model on full x1 and tested on x3. In cross validation you evaluate

[scikit-learn] Fwd: There is a problem with using "r2" to calculate cross_val_score and GridSearchCV scores

I don’t know if the email was successfully sent last time. I send it again now. I’m sorry to disturb you. -- Forwarded message - 发件人： Haylee Miller Date: 2021年12月24日周五 21:17 Subject: There is a problem with using "r2" to calculate cross_val_score and GridSearchCV scores To: Dea

[scikit-learn] Fwd:

-- 转发的邮件 - 发件人： 杨哈哈 日期：2021年11月10日 周三下午10:08 主题： 收件人： Excuse me, I'm a Chinese student, Yang.I want to cite sklearn package in my paper, because it's very efficient to machine learning. But I'm doubt about references in official webpage. For example, the references in '*https:/

Re: [scikit-learn] Fwd: [Numpy-discussion] start of an array (tensor) and dataframe API standardization initiative

Yes, I think that I kickstarted this a few month ago: https://discuss.ossdata.org/t/a-dataframe-protocol-for-the-pydata-ecosystem/267 I really hope that this will help us serve better the community in scikit-learn! G On Tue, Aug 18, 2020 at 05:11:46PM +0200, Adrin wrote: > FYI: Related to the da

[scikit-learn] Fwd: [Numpy-discussion] start of an array (tensor) and dataframe API standardization initiative

FYI: Related to the data-frame like discussions we've been having. -- Forwarded message - From: Ralf Gommers Date: Mon., Aug. 17, 2020, 22:35 Subject: [Numpy-discussion] start of an array (tensor) and dataframe API standardization initiative To: Discussion of Numerical Python H

[scikit-learn] Fwd: Question regarding regression models

Hi scikit-learn, I have a question related to regression models. Please find my question in the link below. I am still new to this and would appreciate any help. Thank you and have a nice day! https://stackoverflow.com/questions/62325079/issues-with-regression-model-giving-inverse-relationship K

Re: [scikit-learn] Fwd: StackingClassifier

Great - thanks! Yes, it would be very nice to have feature names automatically propagate throughout sklearn. Andrew <~~~> J. Andrew Howe, PhD LinkedIn Profile ResearchGate Profile Open

Re: [scikit-learn] Fwd: StackingClassifier

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 na

[scikit-learn] Fwd: StackingClassifier

Hi All - gentle nudge in case anybody has an idea about this. Andrew <~~~> J. Andrew Howe, PhD LinkedIn Profile ResearchGate Profile Open Researcher and Contributor ID (ORCID)

[scikit-learn] Fwd: CutEncoder - simple suggestion for sklearn.preprocessing

-- Forwarded message - From: Ayoub Abozer Date: Fri, Nov 1, 2019 at 1:27 AM Subject: CutEncoder - simple suggestion for sklearn.preprocessing To: Hello. please take a loot to my kaggle notebook. i have a simple suggestion for new encoder for sklearn.preprocessing. https://www.

[scikit-learn] Fwd: [Nairobi, Kenya WiMLDS] OS sprint (June 2019): Impact Report

Hello, (I can't quite figure out how to respond to a thread on the mailing list, maybe because I receive a digest version.) Hi Joel, WiMLDS has 72 chapters worldwide... including... Australia: 1. Sydney (355 members) https://www.meetup.com/Sydney-Women-in-Machine-Learning-and-Data-Science/ 2. M

Re: [scikit-learn] Fwd: [Nairobi, Kenya WiMLDS] OS sprint (June 2019): Impact Report

Awesome work and great write-up, Reshama. Thanks Andy and Adrin especially, for bringing us along in your commitment to such causes. ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

[scikit-learn] Fwd: [Nairobi, Kenya WiMLDS] OS sprint (June 2019): Impact Report

Hello, On March 12, 2019, I had posted to this mailing list looking for a contributor for our Nairobi, WiMLDS sprint: https://mail.python.org/pipermail/scikit-learn/2019-March/003048.html Happy to share the sprint happened (on June 22, 2019) and I just published the Impact Report: https://reshama

Re: [scikit-learn] Fwd: Proposing Encoder class to encode Ordinal attributes

There is this in scikit-learn-contrib, Categorical Encodering: https://joss.theoj.org/papers/d57818316816a19a80112892c3d12ed7 https://github.com/scikit-learn-contrib/categorical-encoding David Nicholson, Ph.D. https://nicholdav.info/ https://github.com/NickleDave Prinz lab

Re: [scikit-learn] Fwd: Proposing Encoder class to encode Ordinal attributes

There has been an issue and a pull request for something similar in DictVectorizer. https://github.com/scikit-learn/scikit-learn/pull/8750 got close to merging and I'm not really sure why it was closed rather than completed. ___ scikit-learn mailing list

[scikit-learn] Fwd: Proposing Encoder class to encode Ordinal attributes

Hi, Can someone please respond. Any response would be appreciated Thanks -- Forwarded message - From: prudhviraj nitjsr Date: Sun, May 12, 2019 at 1:38 AM Subject: Proposing Encoder class to encode Ordinal attributes To: Hi All, Recently, when i was solving some ML problem,

[scikit-learn] Fwd: BIC using GMM.fit and GaussianMixture.fit()

-- Forwarded message -- From: Dixeena Lopez Date: 2 August 2018 at 23:53 Subject: BIC using GMM.fit and GaussianMixture.fit() To: scikit-learn@python.org Dear Sir/Madam, I have tried to fit the data using GaussianMixture.fit() and GMM.fit() and calculated the BIC score. The BIC

Re: [scikit-learn] Fwd: Re: Topic for thesis work on scikit learn

On 01/23/2018 11:09 AM, Gaurav Dhingra wrote: | Tricky thinngs we’ve been trying to do for years: | * estimator tags | * sample props I actually have a student working on estimator tags right now. I'm thinking of choosing https://github.com/scikit-learn/scikit-learn/pull/6948 (ENH o

Re: [scikit-learn] Fwd: Re: Topic for thesis work on scikit learn

Hi Andreas, On Tuesday 23 January 2018 09:12 PM, Gaurav Dhingra wrote: Forwarded Message Subject:Re: [scikit-learn] Topic for thesis work on scikit learn Date: Tue, 23 Jan 2018 10:16:36 -0500 From: Andreas Mueller To: Gaurav Dhingra Hi Gaurav. Is your

[scikit-learn] Fwd: Re: Topic for thesis work on scikit learn

Forwarded Message Subject:Re: [scikit-learn] Topic for thesis work on scikit learn Date: Tue, 23 Jan 2018 10:16:36 -0500 From: Andreas Mueller To: Gaurav Dhingra Hi Gaurav. Is your mentor experienced in contributing to sklearn? Will they be able to revie

Re: [scikit-learn] Fwd: Custom transformer failing check_estimator test

The difference is the functional form versus the context manager. you can't add extra parameters to the function, only to the context manager. On 27 Jul 2017 12:56 am, "Andreas Mueller" wrote: > Hm, it would be nice to do this in a way that relies less on pytest, but I > guess that would be tric

Re: [scikit-learn] Fwd: Custom transformer failing check_estimator test

Apologies: I've since worked out what the problem was and have resolved this issue. This was what I was missing in my code: # Check that the input is of the same shape as the one passed # during fit. if X.shape != self.input_shape_: raise ValueError('Shape of i

Re: [scikit-learn] Fwd: Custom transformer failing check_estimator test

This is the Traceback I get: AssertionErrorTraceback (most recent call last) in () > 1 check_estimator(OK.Sqizer) /Users/Sam/anaconda/lib/python2.7/site-packages/sklearn/utils/estimator_ checks.pyc in check_estimator(Estimator) 253 check_parameters_default_constructible(name, Estima

Re: [scikit-learn] Fwd: Custom transformer failing check_estimator test

what is the failing test? please provide the full traceback. On 24 Jul 2017 10:58 pm, "Sam Barnett" wrote: > Dear scikit-learn developers, > > I am developing a transformer, named Sqizer, that has the ultimate goal > of modifying a kernel for use with the sklearn.svm package. When given an > inp

Re: [scikit-learn] Fwd: Feedback on scikit-learn.org

Obligatory meme: https://imgur.com/a/BLimp Warren On Sat, Jul 8, 2017 at 2:22 AM, Mathieu Blondel wrote: > Someone had this to say about eigenfaces. > > -- Forwarded message -- > From: Frances Liu > Date: Sat, Jul 8, 2017 at 6:57 AM > Subject: Feedback on scikit-learn.org > To:

Re: [scikit-learn] Fwd: Feedback on scikit-learn.org

In the same line, we should stop publishing faces generated by GANs. They are even worse :-) Guillaume Lemaitre  INRIA Saclay Ile-de-France / Equipe PARIETAL guillaume.lemai...@inria.fr - https://glemaitre.github.io/ ___ scikit-learn mailing list sciki

Re: [scikit-learn] Fwd: Feedback on scikit-learn.org

The way that I would think about such a question is that a website like that of the New York Times, or of Le Monde, has pictures that are much more scary. We are probably on the safe end. Cheers, Gaël On Sat, Jul 08, 2017 at 03:22:31PM +0900, Mathieu Blondel wrote: > Someone had this to say abou

[scikit-learn] Fwd: Feedback on scikit-learn.org

Someone had this to say about eigenfaces. -- Forwarded message -- From: Frances Liu Date: Sat, Jul 8, 2017 at 6:57 AM Subject: Feedback on scikit-learn.org To: math...@mblondel.org Hi Mathieu, I found your email on your personal website, which is linked at the top of the author

Re: [scikit-learn] Fwd: [SciPy-User] EuroSciPy 2017 call for contributions - extension of deadline

I am pretty sure this is exactly the kind of presentation that the EuroScipy audience would enjoy. Please submit! -- Olivier ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Fwd: [SciPy-User] EuroSciPy 2017 call for contributions - extension of deadline

Dear Communities, Would it be of interest to the audience to hear a discussion on the state of the art in computational drug discovery model development, which my team and I have done by building on top of Scikit-learn and Matplotlib? Everyday language description of the work and concept: https://

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

We could clarify in the documentation that you can grid-search any (hyper) parameter of a model, but not parameters to fit? Only the values returned by get_params() can be tuned. Only "param_grid" will be searched, not "fit_params". "fit_params" can contain only a single setting. On 06/26/20

[scikit-learn] Fwd: [SciPy-User] EuroSciPy 2017 call for contributions - extension of deadline

Hi everyone, I thought some of you might be interested in this dead line extension. Cheers, N -- Forwarded message -- From: Pierre de Buyl Date: 26 June 2017 at 03:49 Subject: [SciPy-User] EuroSciPy 2017 call for contributions - extension of deadline To: scipy-u...@python.org,

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

I don't think we'll be accepting a pull request adding this feature to scikit-learn. It is too niche. But you should go ahead and modify the search to operate over weightings for your own research. If you feel the documentation can be clarified, a pull request there is welcome. On 26 June 2017 at

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

Yes, I guess most users will be happy without using weights. Some will need to use one single vector, but I am currently researching a weighting method thus my need of evaluating multiple weight vectors. I understand that it seems to be a very specific issue with a simple workaround, most likely

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

Joel is right. In fact, you usually don't want to tune a lot the sample weights: you may leave them default, set them in order to balance classes, or fix them according to some business rule. That said, you can always run a couple of grid searchs changing that sample weights and compare result

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

yes, trying multiple sample weightings is not supported by grid search directly. On 23 Jun 2017 6:36 pm, "Manuel Castejón Limas" wrote: > Dear Joel, > > I tried and removed the square brackets and now it works as expected *for > a single* sample_weight vector: > > validator = GridSearchCV(my_Reg

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

Dear Joel, I tried and removed the square brackets and now it works as expected *for a single* sample_weight vector: validator = GridSearchCV(my_Regressor, param_grid={'number_of_hidden_neurons': range(4, 5), 'epochs': [50],

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

Dear Joel, I'm just passing an iterable as I would do with any other sequence of parameters to tune. In this case the list only has one element to use but in general I ought to be able to pass a collection of vectors. Anyway, I guess that that issue is not the cause of the problem. El 23 jun. 2017

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

Hello Antonio, Sure: import sklearn print(sklearn.__version__) 0.18.1 The error suggests that the fit function is expecting a split vector with size 2/3*1000 but the whole vector (size 1000) is passed. ... ValueError: Found a sample_weight array with shape (1000,) for an input with shape (666,

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

why are you passing [my_sample_weights] rather than just my_sample_weights? On 23 Jun 2017 7:49 am, "Julio Antonio Soto de Vicente" wrote: > Hi Manuel, > > Are you sure that you are using the latest version (or at least >0.17)? > The code for splitting the sample weights in GridSearchCV has been

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

Hi Manuel, Are you sure that you are using the latest version (or at least >0.17)? The code for splitting the sample weights in GridSearchCV has been there for a while now... -- Julio > El 22 jun 2017, a las 23:33, Manuel Castejón Limas > escribió: > > Dear all, > I posted the full question

[scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

Dear all, I posted the full question on StackOverflow and as it contains some figures I refer you to that post. https://stackoverflow.com/questions/44661926/sample- weight-parameter-shape-error-in-scikit-learn-gridsearchcv/44662285#44662285 I currently believe that this issue is a bug and I open

Re: [scikit-learn] Fwd: SSIM with tolerances

I wonder naively: if you can make rules, why train something to learn them vs. just implementing them directly? I'm really curious if there's an advantage in logistics or performance (can meaningful extrapolation somehow occur?). I think the answer for machine learning is not to make rules, bu

[scikit-learn] Fwd: SSIM with tolerances

Hi, I am using SSIM to compare 2 video streams\sets of images and I find it to be almost too accurate. I would like some fudge factor like other image comparison tools have. I used to do it in an automated test suite but due to file sizes and amounts I turned to scikit. I do quality assurance on

Re: [scikit-learn] Fwd: Scikit-learn MLPRegressor Help

On 12/06/2016 06:27 AM, Gael Varoquaux wrote: On Sat, Dec 03, 2016 at 10:35:03PM +, federico vaggi wrote: As long as the feature ordering has a meaningful spatial component (as is almost always the case when you are dealing with raw pixels as features) CNNs will almost always be better. T

Re: [scikit-learn] Fwd: Scikit-learn MLPRegressor Help

On Sat, Dec 03, 2016 at 10:35:03PM +, federico vaggi wrote: > As long as the feature ordering has a meaningful spatial component (as is > almost always the case when you are dealing with raw pixels as features) CNNs > will almost always be better.  There is another important aspect: CNN are me

Re: [scikit-learn] Fwd: Scikit-learn MLPRegressor Help

As long as the feature ordering has a meaningful spatial component (as is almost always the case when you are dealing with raw pixels as features) CNNs will almost always be better. CNNs actually have a lot fewer parameters than MLPs (depending on architecture of course) because of weight sharing

Re: [scikit-learn] Fwd: Scikit-learn MLPRegressor Help

No, I am not saying it is better than CNN, but my images aren't real-life images but computer generated silhouettes. So CNN seemed to be overkill. I'll revisit CNN. I resized the images and converted it to grayscale. Now I am feeding [1,4800] now and I am getting good output with MLP. I looped over

Re: [scikit-learn] Fwd: Scikit-learn MLPRegressor Help

On 12/03/2016 03:10 PM, Alekh Karkada Ashok wrote: Hey All, I chose MLP because they were images and I have heard MLPs perform better. Better than a convolutional neural net? Whoever told you that was wrong. I usually don't make absolute statements like this, but this is something that is p

Re: [scikit-learn] Fwd: Scikit-learn MLPRegressor Help

Hey All, I chose MLP because they were images and I have heard MLPs perform better. My application is detecting body parts from these images and therefore, the mapping would be pretty non-linear and this was my idea behind selecting MLP. Otherwise, I would have to engineer high dimension features

Re: [scikit-learn] Fwd: Scikit-learn MLPRegressor Help

On 12/03/2016 05:29 AM, Gael Varoquaux wrote: On Sat, Dec 03, 2016 at 03:08:00PM +0530, Alekh Karkada Ashok wrote: I want use the Scikit-learn's MLPRegressor to map image to image. That is I have a numpy array of size [1000,2030400] (1000 samples, 76800x3 (RGB) pixels). Corresponding labelled

Re: [scikit-learn] Fwd: Scikit-learn MLPRegressor Help

On Sat, Dec 03, 2016 at 03:08:00PM +0530, Alekh Karkada Ashok wrote: > I want use the Scikit-learn's MLPRegressor to map image to image. That is I > have a numpy array of size [1000,2030400] (1000 samples, 76800x3 (RGB) > pixels). > Corresponding labelled images I have. Therefore Y is also [1000,2

[scikit-learn] Fwd: Scikit-learn MLPRegressor Help

Hi all, I want use the Scikit-learn's MLPRegressor to map image to image. That is I have a numpy array of size [1000,2030400] (1000 samples, 76800x3 (RGB) pixels). Corresponding labelled images I have. Therefore Y is also [1000,230400]. But according to documentation: *fit(X, y)* Fit the model to

Re: [scikit-learn] Fwd: libmf bindings

Given that we'd love to get rid of our libsvm/liblinear biddings, I would be more in favor of improving our matrix factorization code rather than including this code. That said, +1 for missing data imputation with matrix factorization, once we're done with the current PRs on missing data. Gaël O

Re: [scikit-learn] Fwd: libmf bindings

(I am not a scikit learn dev.) This is a great idea and I for one look forward to using it. My understanding is that libmf optimises only over the observed values (that is the explicitly given values in a sparse matrix) as is typically needed for recommender system whereas the scikit learn NMF co

[scikit-learn] Fwd: libmf bindings

Forwarded Message Subject:libmf bindings Date: Wed, 2 Nov 2016 11:38:00 -0400 From: sam royston To: scikit-learn-ow...@python.org Hi, Thanks for all your hard work on this useful tool! I'm hoping to contribute bindings to Chih-Jen Lin's libmf: https://ww

[scikit-learn] Fwd: [Scikit-learn-general] MultinomialNB Scikit-learn question

-- Forwarded message -- From: Bharat Didwania 4-Yr B.Tech. Electrical Engg. < bharat.didwania.ee...@itbhu.ac.in> Date: Mon, Sep 26, 2016 at 11:04 PM Subject: Re: [Scikit-learn-general] MultinomialNB Scikit-learn question To: scikit-learn-gene...@lists.sourceforge.net It seems you

Re: [scikit-learn] Fwd: Featuring scikit-learn in Hacktoberfest 2016

On 09/16/2016 06:10 PM, Nelson Liu wrote: Perhaps my experience is skewed, but I've found that lots of newcomers have difficulty finding something to work on; we'd probably want to improve ourselves in that regard if we were to expect a significant amount of new contributions from this prog

Re: [scikit-learn] Fwd: Featuring scikit-learn in Hacktoberfest 2016

On 09/16/2016 06:02 PM, Jacob Schreiber wrote: I think it's always good to get more contributors, even if they only add small amounts of code and disappear. However, I'd be worried about people submitting low quality PRs in order to claim "success" and/or not following up. One month is a reas

Re: [scikit-learn] Fwd: Featuring scikit-learn in Hacktoberfest 2016

> I'm not sure I want to add a label. +1 on this. I'm also a bit wary of low quality pull requests, but I don't think it will be that common of an occurrence. Perhaps my experience is skewed, but I've found that lots of newcomers have difficulty finding something to work on; we'd probably want to

Re: [scikit-learn] Fwd: Featuring scikit-learn in Hacktoberfest 2016

I think it's always good to get more contributors, even if they only add small amounts of code and disappear. However, I'd be worried about people submitting low quality PRs in order to claim "success" and/or not following up. On Fri, Sep 16, 2016 at 5:59 PM, Andreas Mueller wrote: > Hey all. >

[scikit-learn] Fwd: Featuring scikit-learn in Hacktoberfest 2016

Hey all. Wdyt (see below)? I'm always for new contributors ;) - Though it does add overhead. I'm not sure I want to add a label, but being on the page seems a good thing, I think. Adny Forwarded Message Subject:Featuring scikit-learn in Hacktoberfest 2016 Date: F

[scikit-learn] Fwd: Loading file in libsvm format

-- Forwarded message -- From: klo uo Date: Thu, Sep 8, 2016 at 8:25 PM Subject: Loading file in libsvm format To: scikit-learn-gene...@lists.sourceforge.net Hi, I produced a file in libsvm format: : : ... with this content: 6284 576:1 884:1 2482:1 4279:1 5765:1 18455

Re: [scikit-learn] Fwd: Loading file in libsvm format

The feature are sparse. The third row says 5765159:1 meaning there is a feature with number 5765159. On 09/08/2016 02:40 PM, klo uo wrote: -- Forwarded message -- From: *klo uo* mailto:klo...@gmail.com>> Date: Thu, Sep 8, 2016 at 8:25 PM Subject: Loading file in libsvm format T

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

Any chance it's related to the seed issue in the "Decoding Differences Between SKL SVM and Matlab Libsvm Even When Parameters the Same" thread? Thanks, Michael J. Bommarito II, CEO Bommarito Consulting, LLC *Web:* http://www.bommaritollc.com *Mobile:* +1 (646) 450-3387 On Sun, Aug 28, 2016 at 12:

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

On 08/27/2016 09:48 AM, Joel Nothman wrote: I don't think we should assume that this is the only possible reason for inconsistency. Could you give us a small snippet of data and code on which you find this inconsistency? I would also expect different settings or random states or data prepar

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

One logical possibility is if svm would accept the scikit-learn changes. On 8/27/16 6:42 AM, elge...@gmail.com wrote: So there is no possibility to reach a consistency? 2016-08-27 15:36 GMT+03:00 olologin >: On 08/27/2016 02:19 PM, elge...@gmail.com

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

I don't think we should assume that this is the only possible reason for inconsistency. Could you give us a small snippet of data and code on which you find this inconsistency? On 27 August 2016 at 23:42, elge...@gmail.com wrote: > So there is no possibility to reach a consistency? > > 2016-08-2

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

So there is no possibility to reach a consistency? 2016-08-27 15:36 GMT+03:00 olologin : > On 08/27/2016 02:19 PM, elge...@gmail.com wrote: > > Can I update the libsvm version by myself? > > 2016-08-27 12:49 GMT+03:00 olologin : > >> On 08/27/2016 12:33 PM, elge...@gmail.com wrote: >> >> I have a

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

On 08/27/2016 02:19 PM, elge...@gmail.com wrote: Can I update the libsvm version by myself? 2016-08-27 12:49 GMT+03:00 olologin >: On 08/27/2016 12:33 PM, elge...@gmail.com wrote: I have a project that is based on SVM algorith

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

Can I update the libsvm version by myself? 2016-08-27 12:49 GMT+03:00 olologin : > On 08/27/2016 12:33 PM, elge...@gmail.com wrote: > > I have a project that is based on SVM algorithm implemented by libsvm > . Recently I decided to try > several other

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

On 08/27/2016 12:33 PM, elge...@gmail.com wrote: I have a project that is based on SVM algorithm implemented by libsvm . Recently I decided to try several other classification algorithm, this is where scikit-learn comes

[scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

I have a project that is based on SVM algorithm implemented by libsvm . Recently I decided to try several other classification algorithm, this is where scikit-learn comes to the picture. The connection to the scikit was pretty

Re: [scikit-learn] Fwd: ValueError

I also strongly recommend the Anaconda distribution. Andrew <~~~> J. Andrew Howe, PhD Editor-in-Chief, European Journal of Mathematical Sciences Executive Editor, European Journal of Pure and Applied Mathematics www.andrewhowe.com http://www.linkedin.com/in/ahowe42 https:/

Re: [scikit-learn] Fwd: ValueError

On Wed, Jun 1, 2016 at 11:50 AM, Sebastian Raschka wrote: > Oh that’s interesting, thanks for the info! I have never used the system > Python and didn’t know that it comes with its own NumPy & SciPy; I thought it > was more bare bones (sorry about the naive question, but is this a recent > thin

Re: [scikit-learn] Fwd: ValueError

I am more naive than you ;) I have no clue! Thanks, Ruchika Dr Ruchika Nayyar, Post Doctoral Fellow for ATLAS Collaboration University of Arizona Arizona, USA. On Wed, Jun 1, 2016 at 11:50 AM, Sebastian Raschka

Re: [scikit-learn] Fwd: ValueError

> I just removed the default one that came with python 2.7 As a general advice, I would never ever mess with the system Python; you could easily break something … “important” :P Why not creating a separate virtual environment to be on the safe side? > On Jun 1, 2016, at 2:51 PM, Ruchika Nayyar

Re: [scikit-learn] Fwd: ValueError

Oh that’s interesting, thanks for the info! I have never used the system Python and didn’t know that it comes with its own NumPy & SciPy; I thought it was more bare bones (sorry about the naive question, but is this a recent thing in Yosemite or has it been always like this?) > On Jun 1, 2016,

Re: [scikit-learn] Fwd: ValueError

Much thanks for your advice I was able to sort out the issue with ValueError due to num/scipy I just removed the default one that came with python 2.7 and re-installed latest version. I did the same for matplotlib but if I do this import matplotlib.pyplot as plt It gives me this error: Learning

Re: [scikit-learn] Fwd: ValueError

Just to note, anaconda also has it's own method of managing environments: On Wed, Jun 1, 2016 at 2:43 PM, Andrea Bravi wrote: > > Hi guys, > > > I recommend using https://virtualenv.pypa.io > to solve those issues! > > > Best regards, > > Andrea > > > On W

Re: [scikit-learn] Fwd: ValueError

Hi guys, I recommend using https://virtualenv.pypa.io to solve those issues! Best regards, Andrea On Wednesday, 1 June 2016, Matthew Brett wrote: > On Wed, Jun 1, 2016 at 11:17 AM, Sebastian Raschka > > wrote: > >> I think you're using system Python o

Re: [scikit-learn] Fwd: ValueError

On Wed, Jun 1, 2016 at 11:17 AM, Sebastian Raschka wrote: >> I think you're using system Python on the Mac. I'd really strongly >> recommend against that, because system Python > > Yeah, but I think that the system Python doesn’t come with NumPy and SciPy > installed on a Mac? That's the entir

Re: [scikit-learn] Fwd: ValueError

I also recommend Anaconda. I have installed on several variants of Linux as well as Mac and Windows, and it usually works right out of the box. Anytime it didn't, the issue was nothing that 5 minutes of Googling couldn't solve. I believe that scikit-learn is usually included, and if it isn't their

Re: [scikit-learn] Fwd: ValueError

> I think you're using system Python on the Mac. I'd really strongly > recommend against that, because system Python Yeah, but I think that the system Python doesn’t come with NumPy and SciPy installed on a Mac? Personally, I am using Conda’s dist., not the system Python python --version Pytho

Re: [scikit-learn] Fwd: ValueError

On Wed, Jun 1, 2016 at 11:08 AM, Ruchika Nayyar wrote: > I also copy pasted blindly. Actually I see that both numpy and scipy have > older versions > and not the new ones that I want. Let me look into the thread and do more > debugging. Yes, this is very likely because of the problem I pointed to

Re: [scikit-learn] Fwd: ValueError

Hi, On Wed, Jun 1, 2016 at 11:00 AM, Sebastian Raschka wrote: > Sorry, > > $ python -c 'import numpy; print(scipy.__version__)’ > > was a type, it should be > > $ python -c 'import scipy; print(scipy.__version__)’ > > However, I’d recommend looking at the Issue 6706 as Nelson Liu suggested for >

Re: [scikit-learn] Fwd: ValueError

I also copy pasted blindly. Actually I see that both numpy and scipy have older versions and not the new ones that I want. Let me look into the thread and do more debugging. Thanks, Ruchika Dr Ruchika Nayyar, Post Doctoral Fellow for ATLAS Collaboration Un

Re: [scikit-learn] Fwd: ValueError

Hi, As Nelson suggested, probably the version pip shows and imported in python are not matching. Could you see the output of ``pip freeze`` ? In case that is not 1.8.0rc then the solution in that issue would probably be helpful here. Regards, Maniteja.

Re: [scikit-learn] Fwd: ValueError

Sorry, $ python -c 'import numpy; print(scipy.__version__)’ was a type, it should be $ python -c 'import scipy; print(scipy.__version__)’ However, I’d recommend looking at the Issue 6706 as Nelson Liu suggested for further debugging (https://github.com/scikit-learn/scikit-learn/issues/6706)!

Re: [scikit-learn] Fwd: ValueError

cikit-learn user and developer mailing list Subject: Re: [scikit-learn] Fwd: ValueError ⚠ EXT MSG: Hi , I hope I am understanding the question correctly here. This happens when there is mismatch between numpy installed and the one scikit-learn is compiled with. In case there are multiple versio

Re: [scikit-learn] Fwd: ValueError

Hello Sebastian Thanks for some insight.. So here are some of my responses 1) $ python -c 'import numpy; print(numpy.__version__)' 1.8.0rc1 2) python -c 'import numpy; print(scipy.__version__)' Traceback (most recent call last): File "", line 1, in NameError: name 'scipy' is not defined

Re: [scikit-learn] Fwd: ValueError

Hi Ruchika, See if any of the suggestions in this issue ( https://github.com/scikit-learn/scikit-learn/issues/6706) help, it seems to be the same issue. Try reinstalling scikit-learn as well. Nelson Liu On Wed, Jun 1, 2016 at 10:33 AM Ruchika Nayyar wrote: > > Thanks, > Ruchika > --

Re: [scikit-learn] Fwd: ValueError

Hi , I hope I am understanding the question correctly here. This happens when there is mismatch between numpy installed and the one scikit-learn is compiled with. In case there are multiple versions of numpy installed, it is preferable to put the newest one on PYTHONPATH. If a stable version is ne

Re: [scikit-learn] Fwd: ValueError

Hi Ruchika, could you maybe post the results from $ python -c 'import numpy; print(numpy.__version__)' 1.11.0 $ python -c 'import numpy; print(scipy.__version__)' 0.17.0 just to make sure that these are indeed the latest versions? However, I suspect that this is more of a compile rather than a

[scikit-learn] Fwd: ValueError

Thanks, Ruchika Dr Ruchika Nayyar, Post Doctoral Fellow for ATLAS Collaboration University of Arizona Arizona, USA. -- Forwarded message -- From: Date: Wed, Jun 1, 2016 at 10:23 AM Subject: Value