Mailman 3 July 2018 - scikit-learn

Plot Cross-validated ROCs for multi-class classification problem
by serafim loukas July 20, 2018

July 20, 2018

Dear Scikit-learn community, I have a 3 class classification problem and I would like to plot the average ROC across Folds. There is an example in scikit-learn website but only for binary classification problems (http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_cross…). I want to do the same but in the case of 3 classes. I have tried to use `clf = OneVsRestClassifier(LinearDiscriminantAnalysis())`but I am having a hard time to make it work. Any help would be appreciated, Makis

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sample_weights in RandomForestRegressor
by Thomas Evangelidis July 16, 2018

July 16, 2018

Hello, I am kind of confused about the use of sample_weights parameter in the fit() function of RandomForestRegressor. Here is my problem: I am trying to predict the binding affinity of small molecules to a protein. I have a training set of 709 molecules and a blind test set of 180 molecules. I want to find those features that are more important for the correct prediction of the binding affinity of those 180 molecules of my blind test set. My rationale is that if I give more emphasis to the similar molecules in the training set, then I will get higher importances for those features that have higher predictive ability for this specific blind test set of 180 molecules. To this end, I weighted the 709 training set molecules by their maximum similarity to the 180 molecules, selected only those features with high importance and trained a new RF with all 709 molecules. I got some results but I am not satisfied. Is this the right way to use sample_weights in RF. I would appreciate any advice or suggested work flow. -- ====================================================================== Dr Thomas Evangelidis Post-doctoral Researcher CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/2S049, 62500 Brno, Czech Republic email: tevang(a)pharm.uoa.gr tevang3(a)gmail.com website: https://sites.google.com/site/thomasevangelidishomepage/

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Finding Formula of Gaussian Process Classification
by August Morin July 12, 2018

July 12, 2018

Hi all, I've been handed down some code that is based on the Classifier Comparison done by Gaël Varoquaux and Andreas Müller. The dataset is best classified by the Gaussian Process, from which I would like to be able to find a formula that I can run other datasets through for an image filtering project. Is there a way to export the formula directly from sklearn? Any ideas are much appreciated.

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compiling issue
by Marco Fronzi July 10, 2018

July 10, 2018

Hi, My name is Marco and I am trying to install scikit-learn on my mac (OX 10.11.6). I installed already python3, numpy (1.8.2) and scipy, however when I run pip3 scikit-learn I get several errors which are listed below. Failed building wheel for scikit-learn and also: Command "/usr/local/opt/python/bin/python3.7 -u -c "import setuptools, tokenize;__file__='/private/tmp/pip-install-4z67z8of/ scikit-learn/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /private/tmp/pip-record-pbcsv1zz/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /private/tmp/pip-install-4z67z8of/scikit-learn/ I would appreciate any suggestion/hint to solve this issue and the package. Thank you, Marco

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Next sprint in Paris (july 16th and 17th)
by Jeremie du Boisberranger July 4, 2018

July 4, 2018

Hi everyone, On july 16th and 17th, there will be a scikit-learn sprint in Paris, in parallel with the one in Austin. There will be an official announce soon with the location and other informations. This is just an informal mail to ask if you have suggestions on topics/issues that you think we should look at during the sprint. Remember that it is a 2 days sprint, so we need things that can be handled in 2 days. Whether you intend to come or not, any suggestion is welcomed ! Best regards, Jeremie du Boisberranger <jeremiedbb>

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Re: [scikit-learn] Update or downgrade PCA
by Pamphile Roy July 3, 2018

July 3, 2018

I have no idea about the comparison with sklearn.decomposition.IncrementalPCA. Was not aware of this but from the code it seems to be a different approach. I will try to come with some numbers. Pamphile

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Supervised prediction of multiple scores for a document
by Amirouche Boubekki July 3, 2018

July 3, 2018

Héllo, I started a natural language processing project a few weeks ago called wikimark <https://github.com/amirouche/wikimark/> (the code is all in wikimark.py <https://github.com/amirouche/wikimark/blob/master/wikimark.py#L1>) Given a text it wants to return a dictionary scoring the input against vital articles categories <https://en.wikipedia.org/api/rest_v1/page/html/Wikipedia%3AVital_articles%2…>, e.g.: out = wikimark("""Peter Hintjens wrote about the relation between technology and culture. Without using a scientifical tone of state-of-the-art review of the anthroposcene antropology, he gives a fair amount of food for thought. According to Hintjens, technology is doomed to become cheap. As matter of fact, intelligence tools will become more and more accessible which will trigger a revolution to rebalance forces in society.""") for category, score in out: print('{} ~ {}'.format(category, score)) The above program would output something like that: Art ~ 0.1 Science ~ 0.5 Society ~ 0.4 Except not everything went as planned. Mind the fact that in the above example the total is equal to 1, but I could not achieve that at all. I am using gensim to compute vectors of paragraphs (doc2vev) and then submit those vectors to svm.SVR in a one-vs-all strategy ie. a document is scored 1 if it's in that subcategory and zero otherwise. At prediction time, it goes though the same doc2vec pipeline. The computer will score *each paragraph* against the SVR models of wikipedia vital article subcategories and get a value between 0 and 1 for *each paragraph*. I compute the sum and group by subcategory and then I have a score per category for the input document It somewhat works. I made a web ui online you can find it at https://sensimark.com where you can test it. You can directly access the full api e.g. https://sensimark.com/api/v0?url=http://www.multivax.com/last_question.html… The output JSON document is a list of category dictionary where the prediction key is associated with the average of the "prediction" of the subcategories. If you replace &all=1 by &top=5 you might get something else as top categories e.g. https://sensimark.com/api/v0?url=http://www.multivax.com/last_question.html… <https://sensimark.com/api/v0?url=http://www.multivax.com/last_question.html…> or https://sensimark.com/api/v0?url=http://www.multivax.com/last_question.html… I wrote "prediction" with double quotes because the value you see, is the result of some formula. Since, the predictions I get are rather small between 0 and 0.015 I apply the following formula: value = math.exp(prediction) magic = ((value * 100) - 110) * 100 In order to have values to spread between -200 and 200. Maybe this is the symptom that my model doesn't work at all. Still, the top 10 results are almost always near each other (try with BBC <http://www.bbc.com/> articles on https://sensimark.com . It is only when a regression model is disqualified with a score of 0 that the results are simple to understand. Sadly, I don't have an example at hand to support that claim. You have to believe me. I just figured looking at the machine learning map <http://scikit-learn.org/stable/tutorial/machine_learning_map/> that my problem might be classification problem, except I don't really want to know what is *the* class of new documents, I want to how what are the different subjects that are dealt in the document based on a hiearchical corpus; I don't want to guess a hiearchy! I want to now how the document content spread over the different categories or subcategories. I quickly read about multinomal regression, is it something do you recommend I use? Maybe you think about something else? Also, it seems I should benchmark / evaluate my model against LDA. I am rather noob in terms of datascience and my math skills are not so fresh. I more likely looking for ideas on what algorithm, fine tuning and some practice of datascience I must follow that doesn't involve writing my own algorithm. Thanks in advance!

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Update or downgrade PCA
by Pamphile Roy July 3, 2018

July 3, 2018

Hi everyone, I have some code that allows to upgrade (or downgrade) a PCA with a new sample. The update part is handy when you are doing live observations for instance and you want a quick way to update your PCA without having to recompute the whole thing from scratch. Are you interested in this? (For me or someone else to integrate it.) Code is open-source (from my Batman project) and can be found here: https://gitlab.com/cerfacs/batman/blob/develop/batman/pod/pod.py Functions of interest are _upgrade and downgrade. Although, the code should be cleaned up, it works well and it got some unit tests. Of course the math is backed-up by some literature: [1] M. Brand: Fast low-rank modifications of the thin singular value decomposition. 2006. DOI:10.1016/j.laa.2005.07.021 [2] T. Braconnier: Towards an adaptive POD/SVD surrogate model for aeronautic design. Computers & Fluids. 2011. DOI:10.1016/j.compfluid.2010.09.002 Cheers, Pamphile @tupui

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NEP: Random Number Generator Policy
by Robert Kern July 2, 2018

July 2, 2018

https://mail.python.org/pipermail/numpy-discussion/2018-June/078126.html Hi, sklearners! I have a NEP out for discussion that proposes a change in numpy.random's stream-compatibility policy. As scikit-learn is a well-disciplined consumer of reproducible streams, I would appreciate your input on the numpy-discussion thread linked above. The very short form is that there is a new PRNG subsystem being developed with better core PRNGs (among other things, providing nice features like independent streams for parallel computations), and we would like to relax our strict stream-compatibility policy for the non-uniform distributions in this new subsystem so that we can improve our algorithms. The core uniform numbers would still be strictly stream-compatible across numpy versions. But we would like to be able to upgrade our non-uniform algorithms, for example, to make normal variates faster to generate. RandomState would be frozen and subject to a long deprecation cycle for a period of strict backwards compatibility. There would be some non-deprecated provision to get strictly-compatible streams for a subset of distributions for the limited purpose of generating test data for unit tests. Please read the NEP and the thread through. I do propose at least one alternative in the thread and would like some feedback on it. I would also appreciate it if we could consolidate the discussion on the numpy-discussion thread and not have a split-off conversation here too. Thank you very much! I appreciate your attention. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco

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