🌐 Mailman 3 April 2018 - scikit-learn - python.org

One-hot encoding
by Sarah Wait Zaranek Aug. 3, 2018

Aug. 3, 2018

Hello - I was just wondering if there was a way to improve performance on the one-hot encoder. Or, is there any plans to do so in the future? I am working with a matrix that will ultimately have 20 million categorical variables, and my bottleneck is the one-hot encoder. Let me know if this isn't the place to inquire. My code is very simple when using the encoder, but I cut and pasted it here for completeness. enc = OneHotEncoder(sparse=True) Xtrain = enc.fit_transform(tiledata) Thanks, Sarah

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Delegating "get_params" and "set_params" to a wrapped estimator when parameter is not defined.
by Javier López May 18, 2018

May 18, 2018

I have a class `FancyEstimator(BaseEstimator, MetaEstimatorMixin): ...` that wraps around an arbitrary sklearn estimator to add some functionality I am interested about. This class contains an attribute `self.estimator` that contains the wrapped estimator. Delegation of the main methods, such as `fit`, `transform` works just fine, but I am having some issues with `get_params` and `set_params`. The main idea is, I would like to use my wrapped class as a drop-in replacement for the original estimator, but this raises some issues with some functions that try using the `get_params` and `set_params` straight in my class, as the original parameters now have prefixed names (for instance `estimator__verbose` instead of `verbose`) I would like to delegate calls of set_params and get_params in a smart way so that if a parameter is unknown for my wrapper class, then it automatically goes looking for it in the wrapped estimator. I am not concerned about my class parameter names as there are only a couple of very specific names on it, so it is safe to assume that any unknown parameter name should refer to the base estimator. Is there an easy way of doing that? Cheers, J

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Probability estimation
by ajay sahu April 19, 2018

April 19, 2018

Thanks for making such a brilliant scikit learn code , i have some doubts for a project , can you all tell me how to achieve that using scikit learn or any of your methods .. PROBLEM STATEMENT - i am trying to create a probability tool for creating scores of importance eg (9/10, 8/10 or 6/10 ) depending on the degree of importance ,from a book text corpus , i am trying to calculate the probability of certain topic coming in the exam using previous year question papers . i have around 10 yrs of question papers comprising of 300 question in total from the same syllabus . but books are different . since the questions are not straight forward , i am unable to do this WHAT HAVE I TRIED - i tried using topic modelling and LDA method in it ,but it gives only important topics or words from a text corpus . i was able to generate important topics from text , using GENSIM library but it couldn't solve my problem of matching and comparing it with the questions from the previous years question papers as some times questions were indirect or twisted . WHAT I AM HOPING - i am hoping is there a way scikit learn library can help me with understanding the indirect questions or twisted questions to generate a topic which may help in matching it with the topics in book corpus from the same syllabus thus generating a probability of topics occuring in the exam using previous years question papers . Looking forward for your help , any kind of help in solving this particular problem statement would be of great help to us Thanks -- - *Regards* Ajay

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MLPClassifier - Softmax activation function
by Daniel Baláček April 18, 2018

April 18, 2018

Hello everyone I have a question regarding MLPClassifier in sklearn. In the documentation in section *1.17. Neural network models (supervised) - 1.17.2 Classification* it is stated that "*MLPClassifier* supports multi-class classification by applying Softmax <https://en.wikipedia.org/wiki/Softmax_activation_function> as the output function." However it is not clear how to apply the Softmax function. The way I think (or hope) this works is that if a parameter *activation *is set to *activation = 'logistic' *Softmax function should be automatically applied whenever there are more than two classes. Is this right or does one have to explicitly specify the use of Softmax function in some way? I am sorry if this is a nonsense question. I am new to scikit-learn and machine learning in general and I was not sure about this one. Thank you for any answers in advance. With regards, D. B.

2 1

CFP: The first International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2018)
by MLCN Workshop April 18, 2018

April 18, 2018

*** Apologize for multiple posting *** Dear Colleagues, We are delighted to invite you to join us for the MLCN 2018 <https://mlcn2018.com/> workshop as a satellite event of MICCAI 2018 <http://www.miccai2018.org/en/> conference, Granada, Spain. ------------------------------------------------------------ ------------------------------------------------------ CALL FOR PAPERS: Recent advances in neuroimaging and statistical machine learning provide an exceptional opportunity for investigators and physicians to discover complex relationships between brain, behaviors, and mental and neurological disorders. MLCN 2018 workshop, as a satellite event of MICCAI 2018, aims to bring together researchers in both theory and application from various fields in domains of spatial statistics, pattern recognition in neuroimaging, and predictive clinical neuroscience. Topics of interests include but are not limited to: - Applications of spatio-temporal modeling in predictive clinical neuroscience - Spatial regularization in decoding clinical neuroimaging data - Spatial statistics in neuroimaging - Learning with structured inputs and outputs in clinical neuroscience - Multi-task learning in analyzing structured neuroimaging data - Deep learning in analyzing structured neuroimaging data - Model stability and interpretability in clinical neuroscience ------------------------------------------------------------ --------------------------------------------------------- CONFIRMED SPEAKERS: Christos Davatzikos (University of Pennsylvania) Gaël Varoquaux (Parietal team, INRIA) Jian Kang (University of Michigan) ------------------------------------------------------------ ---------------------------------------------------------- SUBMISSION PROCESS: The workshop seeks high quality, original, and unpublished work on algorithms, theory, and applications of machine learning in clinical neuroimaging and spatially structured data analysis. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style of up to 8-pages papers using the CMT system at https://cmt3.research.microsoft.com/MLCN2018. This workshop uses a double-blind review process in the evaluation phase, thus authors must ensure anonymous submissions. Accepted papers will be published in a joint proceeding with the MICCAI conference. ------------------------------------------------------------ ----------------------------------------------------------- IMPORTANT DATES: Paper submission deadline: June 11, 2018 Notification of Acceptance: July 16, 2018 Camera-ready Submission: July 23, 2018 Workshop Date: September 20, 2018 ------------------------------------------------------------ ------------------------------------------------------ Best regards, MLCN 2018 Organizing Committee, Email: mlcnworkshop(a)gmail.com Website: https://mlcn2018.com/ twitter: @MLCN2018

1 0

Issues with kmeans: Difference in centroid values
by Sidak Pal Singh April 16, 2018

April 16, 2018

Hi everyone, I was using scikit-learn KMeans algorithm to cluster pretrained word-vectors. There are a few things which I found to be surprising and wanted to get some feedback on. - Based upon the 'labels_' assigned to each word-vector (i.e. cluster memberships), I compute every cluster centroid as the average of the word-vectors (corresponding to that cluster). Surprisingly, this seems to be pretty different from the 'cluster_centers_'. Is there anything that I am missing here? - I was later using the verbose option to see if the clustering has converged or not. I saw on the console log messages such as *"**center shift 7.994126e-04 within tolerance 1.243425e-06"*. It seems that this corresponds to some code in *kmeans_elkan.pyx* ( https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cluster/_k… ). - Lastly, another thing that seems strange is that I hadn't set the tolerance value. So the default of 1e-4 should have been used. But if you look again at the above log, it says *within tolerance 1.243425e-06 instead of 1e-4. * It would be great if you can look into this and help me out. Thank you so much! :) Best, Sidak Pal Singh EPFL

2 1

K Medoids Clustering Implementation
by Zane DuFour April 15, 2018

April 15, 2018

Is someone working on an implementation of K-Medoids clustering <https://en.wikipedia.org/wiki/K-medoids> at the moment? If not, I would like to implement it in sklearn. Thanks, Zane Dufour

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Finding a single cluster in 1d data
by Raphael C April 14, 2018

April 14, 2018

I have a set of points in 1d represented by a list X of floating point numbers. The list has one dense section and the rest is sparse and I want to find the dense part. I can't release the actual data but here is a simulation: N = 100 start = 0 points = [] rate = 0.1 for i in range(N): points.append(start) start = start + random.expovariate(rate) rate = 10 for i in range(N*10): points.append(start) start = start + random.expovariate(rate) rate = 0.1 for i in range(N): points.append(start) start = start + random.expovariate(rate) plt.hist(points, bins = 100) plt.show() I would like to use scikit learn to find the dense region. This feels a little like outlier detection or the task of finding one cluster with noise. Is there a suitable method in scikit learn for this task? Raphael

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Re: [scikit-learn] Delegating "get_params" and "set_params" to a wrapped estimator when parameter is not defined.
by Andreas Mueller April 13, 2018

April 13, 2018

Please stay on the mailing list :) I'm not sure if ValueError is the right error that would be raised (I think it's not). And it's hard to say if this will break something in some edge cases. I would probably rather explicitly encode the parameters of FancyEstimator instead of the try, or get them using super.get_params. You also should rewrite get_params. In principle something like that should work, but I wouldn't go as far as saying it's "safe" and you should test it extensively. On 04/13/2018 12:41 PM, Javier López wrote: > Is something like this safe, or might I be breaking some important > functionality? > > ``` > def set_params(self, **params): > try: > super(FancyEstimator, self).set_params(**params) > except ValueError: > self.wrapped_estimator_.set_params(**params) > ``` > > > On Fri, Apr 13, 2018 at 5:05 PM Andreas Mueller <t3kcit(a)gmail.com > <mailto:t3kcit@gmail.com>> wrote: > > You just need to implement get_params and set_params yourself to > delegate in this way, right? > > > On 04/13/2018 11:51 AM, Javier López wrote: >> I have a class >> `FancyEstimator(BaseEstimator, MetaEstimatorMixin): ...` that wraps >> around an arbitrary sklearn estimator to add some functionality I >> am interested about. >> This class contains an attribute `self.estimator` that contains >> the wrapped estimator. >> Delegation of the main methods, such as `fit`, `transform` works >> just fine, but I am >> having some issues with `get_params` and `set_params`. >> >> The main idea is, I would like to use my wrapped class as a >> drop-in replacement for >> the original estimator, but this raises some issues with some >> functions >> that try using the `get_params` and `set_params` straight in my >> class, as the original >> parameters now have prefixed names (for instance >> `estimator__verbose` instead of `verbose`) >> I would like to delegate calls of set_params and get_params in a >> smart way so that if a >> parameter is unknown for my wrapper class, then it automatically >> goes looking for it in >> the wrapped estimator. >> >> I am not concerned about my class parameter names as there are >> only a couple of very >> specific names on it, so it is safe to assume that any unknown >> parameter name should >> refer to the base estimator. Is there an easy way of doing that? >> >> Cheers, >> J >> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn(a)python.org <mailto:scikit-learn@python.org> >> https://mail.python.org/mailman/listinfo/scikit-learn >

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Pipegraph feedback
by Manuel Castejón Limas April 13, 2018

April 13, 2018

Hi all! As you know by now :-) we submitted PipeGraph as a contrib-project proposal. We believe that this tool can be interesting not only for end users wanting to encapsulate their arbitrarily complex workflows but also for sklearn developers as some internal developments could be easily expressed as pipegraphs, for example, ensemble methods. We would love to have some feedback in terms of: - whether we would have to change anything in order to be more in line with sklearn's philosophy - any development you core developers are working on that could be treated as a pipegraph - possible scenarios not implemented yet by pipegraph, such as recurrent graphs, that might be potentially useful. Moreover, in case any core developer is interested in joining the project you are more than welcome! This would provide a great opportunity for collaboration! Best wishes Manuel Castejón-Limas

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