Mailman 3 August 2016 - scikit-learn

Speeding up RF regressors
by Ali Zude Aug. 11, 2016

Aug. 11, 2016

Hi all, I've 6 RF models and I am using them online to predict 6 different variables (using the same features), models quality (error in test data is good). However, the online prediction is very very slow. How can I speed up the prediction? - Can I import models into C++ code? - Is it useful to upgrade to scikit-learn 0.18? and then use multi-output models? - Is sklearn-compiledtreesuseful, they are claiming that it will speed the prediction (5x-8x)times? - I could not use because of array2d error >>PyPi Thank you for your help RegardsAli

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Disable Travis Cache
by Raghav R V Aug. 8, 2016

Aug. 8, 2016

Could someone disable the Travis cache once and for all please? I have seen several frustrating incidents where the Travis fails the PR because of this caching of old files. I also don't understand why it is enabled in the first place. It would really be super helpful if it is disabled for good. Also refer - https://github.com/scikit-learn/scikit-learn/issues/7094 **cc**: Olivier, Andy

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Supervised anomaly detection in time series
by Amita Misra Aug. 8, 2016

Aug. 8, 2016

Hi, I am currently exploring the problem of speed bump detection using accelerometer time series data. I have extracted some features based on mean, std deviation etc within a time window. Since the dataset is highly skewed ( I have just 5 positive samples for every > 300 samples) I was looking into One ClassSVM covariance.EllipticEnvelope sklearn.ensemble.IsolationForest but I am not sure how to use them. What I get from docs separate the positive examples and train using only negative examples clf.fit(X_train) and then predict the positive examples using clf.predict(X_test) I am not sure what is then the role of positive examples in my training dataset or how can I use them to improve my classifier so that I can predict better on new samples. Can we do something like Cross validation to learn the parameters as in normal binary SVM classification Thanks,? Amita Amita Misra Graduate Student Researcher Natural Language and Dialogue Systems Lab Baskin School of Engineering University of California Santa Cruz -- Amita Misra Graduate Student Researcher Natural Language and Dialogue Systems Lab Baskin School of Engineering University of California Santa Cruz

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Scaling model selection on a cluster
by Vlad Ionescu Aug. 8, 2016

Aug. 8, 2016

Hello, I am interested in scaling grid searches on an HPC LSF cluster with about 60 nodes, each with 20 cores. I thought i could just set n_jobs=1000 then submit a job with bsub -n 1000, but then I dug deeper and understood that the underlying joblib used by scikit-learn will create all of those jobs on a single node, resulting in no performance benefits. So I am stuck using a single node. I've read a lengthy discussion some time ago about adding something like this in scikit-learn: https://sourceforge.net/p/scikit-learn/mailman/scikit-learn-general/thread/… However, it hasn't materialized in any way, as far as I can tell. Do you know of any way to do this, or any modern cluster computing libraries for python that might help me write something myself (I found a lot, but it's hard to tell what's considered good or even still under development)? Also, are there still plans to implement this in scikit-learn? You seemed to like the idea back then.

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BM25 Pull Request
by Basil Beirouti Aug. 4, 2016

Aug. 4, 2016

Hi all, Just sending an email for visibility. I've made a pull request to add Bm25 capabilities to complement TFIDF in feature_extraction.text. All tests pass. Sincerely, Basil Beirouti

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StackOverflow Documentation
by Joel Nothman Aug. 4, 2016

Aug. 4, 2016

StackOverflow has introduced its Documentation space, where scikit-learn is a covered subject: http://stackoverflow.com/documentation/scikit-learn. The project is a little interesting, and otherwise somewhat exasperating/tiring, given the overlap with our own documentation efforts, which we would like to see continually improve and maintain alignment with the codebase. Currently there seem to be two contributors. One <http://stackoverflow.com/users/3580365/gal-dreiman> appears to have been copy-pasting official scikit-learn documentation, while the other <http://stackoverflow.com/users/3510736/ami-tavory> has produced original material. From a license perspective, copy-pasted material might be okay with attribution and reference to a BSD licence, with the assumption that it is then double-licensed (BSD and CC-BY-SA) if copied from SO. But I assume that copying without attribution is actually plagiarism and should be reverted, while we should discourage copying with attribution: if SO Documentation for scikit-learn has its place, it should be different to the official reference...?

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Is there any official position on PEP484/mypy?
by Daniel Moisset Aug. 4, 2016

Aug. 4, 2016

Hi, [If you're also on the numpy mailing list and get a similar version of the message, I apologise for that] I work at Machinalis were we use a lot of scikit-learn (and the pydata stack in general). Recently we've also been getting involved with mypy, which is a tool to type check (not on runtime, think of it as a linter) annotated python code (the way of annotating python types has been recently standarized in PEP 484). As part of that involvement we've started creating type annotations for the Python libraries we use most, which include both numpy and scikit-learn. Mypy provides a way to specify types with annotations in separate files in case you don't have control over a library, so we have created an initial proof of concept for numpy at [1], and we are actively improving it. You can find some additional information about it and some problems we've found on the way at this blogpost [2]. We were planning to also start some work on scikit-learn (which has a much larger surface area than numpy, so probably focusing on small parts for now); we had to start with numpy anyway given that SKL depends on it. What I wanted to ask is if the people involved on the SKL project are aware of PEP484 annotations and if you have some interest in starting using them. The main benefit is that annotations serve as clear (and automatically testable) documentation for users, and secondary benefits is that users discovers bugs more quickly and that some IDEs (like pycharm) are starting to use this information for smart editor features (autocompletion, online checking, refactoring tools); eventually tools like jupyter could take advantage of these annotations in the future. And the cost of writing and including these are relatively low. We're doing the work anyway, but contributing our typespecs back could make it easier for users to benefit from this, and for us to maintain it and keep it in sync with future releases. If you've never heard about PEP484 or mypy (it happens a lot) I'll be happy to clarify anything about it that might helpunderstand this situation Thanks! D. [1] https://github.com/machinalis/mypy-data [2] http://www.machinalis.com/blog/writing-type-stubs-for-numpy/ -- Daniel F. Moisset - UK Country Manager www.machinalis.com Skype: @dmoisset

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Model trained in 0.17 gives entirely different results in 0.15
by Shi Yu Aug. 3, 2016

Aug. 3, 2016

Hello, We trained SVM models in scikit-learn 0.17 and saved it as pickle files. When loading the models back in a lower version of scikit-learn 0.15, the outputs are entirely different. Basically for binary classification problem, for the same test data, it swapped the probabilities and gave an opposite prediction. In 0.17 the probability is [0.02668825, 0.97331175] and the prediction is 1. In 0.15 the probability is [0.97331175, 0.02668825] and the prediction is 0. I wonder is anyone seeing the same issue, or it has been notified. I could provide more details for error replication if required. Best, Shi

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Install sklearn into a specific folder to make some changes
by luizfgoncalves＠dcc.ufmg.br Aug. 2, 2016

Aug. 2, 2016

I'm looking for the best way to install sklearn into a specific folder so I can make changes for my work, without worrying about bugging my main sklearn installation (as I use the default version for some experiments too). I tried to clone the git repository and use "python setup.py install", but I'm afraid it will change my user installation too. Right now, what I want is to edit a file called splitter.pyx (on tree folder), compile/install sklearn so it will work with my changes, and test it. What is the best way to do it without causing problems with my main sklearn installation? Thanks a lot for your attention

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