Mailman 3 January 2017 - scikit-learn

numpy integration with random forrest implementation
by Carlton Banks Jan. 21, 2017

Jan. 21, 2017

Hi guys.. I am currently working on a ASR project in which the objective is to substitute part of the general ASR framework with some form of neural network, to see whether the tested part improves in any way. I started working with the feature extraction and tried, to make a neural network (NN) that could create MFCC features. I already know what the desired output is supposed to be, so the problem boils down to a simple input - output mapping. Problem here is the my NN doesn’t seem to perform that well.. and i seem to get pretty large error for some reason. I therefore wanted to give random forrest a try, and see whether it could provide me a better result. I am currently storing my input and output in numpy.ndarrays, in which the input and output columns is consistent throughout all the examples, but the number of rows changes depending on length of the audio file. Is it possible with the random forrest implementation in scikit-learn to train a random forrest to map an input an output, given they are stored numpy.ndarrays? Or do i have do it in a different way? and if so how? kind regards Carl truz

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numpy integration with random forrest implementation
by Carlton Banks Jan. 21, 2017

Jan. 21, 2017

Hi guys.. I am currently working on a ASR project in which the objective is to substitute part of the general ASR framework with some form of neural network, to see whether the tested part improves in any way. I started working with the feature extraction and tried, to make a neural network (NN) that could create MFCC features. I already know what the desired output is supposed to be, so the problem boils down to a simple input - output mapping. Problem here is the my NN doesn’t seem to perform that well.. and i seem to get pretty large error for some reason. I therefore wanted to give random forrest a try, and see whether it could provide me a better result. I am currently storing my input and output in numpy.ndarrays, in which the input and output columns is consistent throughout all the examples, but the number of rows changes depending on length of the audio file. Is it possible with the random forrest implementation in scikit-learn to train a random forrest to map an input an output, given they are stored numpy.ndarrays? Or do i have do it in a different way? and if so how? kind regards Carl truz

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Identify spectra with "marker"
by Sebastian Illner Jan. 21, 2017

Jan. 21, 2017

Hi guys, I'm new to NIR-measurement as wenn as chemometrics. My current project involvs the recognition of determined spectra (of a reference system) among others. The materials are currentlys not really set. So I try to give a predetermined mixture of substances into another matrix and group the measured NIR-spectra according to a) contains predetermined mixture and b) does not contain mixture (but other mixtures could be possible). This way the mixture could be used as a unique marker. What would be the best chemometric way to accomplish this task? Currently I am trying to use PLS-DA, SMC and PCA (combined with a distance quantifier). Thanks for your thought about this. seb

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Pipeline conventions for wrappers
by Aaditya Jamuar Jan. 21, 2017

Jan. 21, 2017

Hi Guys, I am currently working on gensim ( https://github.com/RaRe-Technologies/gensim) , writing wrappers for Scikit-learn for easy integration of LDA ( https://github.com/RaRe-Technologies/gensim/pull/932/files). While I have covered most of the API conventions as specified on scikit-learn's website, I am stuck at how to implement pipelines. I am particularly looking for what are some of the conventions very specific to the pipeline architecture. Please suggest Thank you Aaditya Jamuar

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PR #8190: "Implement Complement Naive Bayes."
by Michael Alcorn Jan. 20, 2017

Jan. 20, 2017

Hi all, I would appreciate it if a couple of maintainers could take a look at my pull request (https://github.com/scikit-learn/scikit-learn/pull/8190) implementing the Complement Naive Bayes (CNB) classifier described in Rennie et al. (2003). CNB regularly outperforms the standard Multinomial Naive Bayes (MNB) classifier on real world data sets due to the tendency for real world data sets to suffer from class imbalance. Apache Mahout offers its own implementation of CNB alongside MNB, but it would be nice to have an easily usable CNB implementation available in scikit-learn. Training the CNB classifier on a reasonably sized data set of 493,038 documents with a median length of 87 tokens and 1,155,784 distinct tokens took around 8.5 seconds. For comparison, the MNB classifier took around 4.5 seconds to train, but the CNB had a 10% lower error rate, a seemingly worthwhile tradeoff. Happy to answer any questions or discuss further. Thanks, Michael A. Alcorn

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GSOC 2017: NumFOCUS will be an umbrella organization
by Max Linke Jan. 16, 2017

Jan. 16, 2017

Hi Organizations can start submitting applications for Google Summer of Code 2017 on January 19 (and the deadline is February 9) https://developers.google.com/open-source/gsoc/timeline?hl=en NumFOCUS will be applying again this year. If you want to work with us please let me know and if you apply as an organization yourself or under a different umbrella organization please tell me as well. If you participate with us it would be great if you start to add possible projects to the ideas page on github soon. We some general information for mentors on github. https://github.com/numfocus/gsoc/blob/master/CONTRIBUTING-mentors.md We also have a template for ideas that might help. It lists the things Google likes to see. https://github.com/numfocus/gsoc/blob/master/2017/ideas-list-skeleton.md In case you participated in earlier years with NumFOCUS there are some small changes this year. Raniere won't be the admin this year. Instead I'm going to be the admin. We are also planning to include two explicit rules when a student should be failed, they have to communicate regularly and commit code into your development branch at the end of the summer. best, Max

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meta-estimator for multiple MLPRegressor
by Thomas Evangelidis Jan. 16, 2017

Jan. 16, 2017

Greetings, I have trained many MLPRegressors using different random_state value and estimated the R^2 using cross-validation. Now I want to combine the top 10% of them in how to get more accurate predictions. Is there a meta-estimator that can get as input a few precomputed MLPRegressors and give consensus predictions? Can the BaggingRegressor do this job using MLPRegressors as input? Thanks in advance for any hint. Thomas -- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 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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Roc curve from multilabel classification has slope
by José Ismael Fernández Martínez Jan. 12, 2017

Jan. 12, 2017

Hi, I have a multilabel classifier written in Keras from which I want to compute AUC and plot a ROC curve for every element classified from my test set. Everything seems fine, except that some elements have a roc curve that have a slope as follows: I don't know how to interpret the slope in such cases. Basically my workflow goes as follows, I have a pre-trained model, instance of Keras, and I have the features X and the binarized labels y, every element in y is an array of length 1000, as it is a multilabel classification problem each element in y might contain many 1s, indicating that the element belongs to multiples classes, so I used the built-in loss of binary_crossentropy and my outputs of the model prediction are score probailities. Then I plot the roc curve as follows. The predict method returns probabilities, as I'm using the functional api of keras. Does anyone knows why my roc curves looks like this? Ismael Sent from my iPhone

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Specify boosting percentage using Randomoversampling?
by Suranga Kasthurirathne Jan. 10, 2017

Jan. 10, 2017

Hi all, I apologize - i've been looking for this answer all over the internet, and it could be that I'm not googling the right terms. For managing unbalanced datasets, Weka has SMOTE, and scikit has randomoversampling. In weka, we can ask it to boost by a given percentage (say 100%) so an undersampled class with 10 values ends up with 20 values (100% increase) after boosting. In Scikit learn, I cant seem to find a way to do this. The ramdomoversampler boosts arbitrarily. and seem to try to balance the two classes, which may not be realistic in some cases. Can anyone point me to how I can manage boosting percentage using scikit? -- Best Regards, Suranga

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Re: [scikit-learn] Generalized Discriminant Analysis with Kernel
by Raga Markely Jan. 10, 2017

Jan. 10, 2017

Thank you very much for your info on Nystroem kernel approximator. I appreciate it! Best, Raga On Tue, Jan 10, 2017 at 7:47 AM, <scikit-learn-request(a)python.org> wrote: > Send scikit-learn mailing list submissions to > scikit-learn(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scikit-learn > or, via email, send a message with subject or body 'help' to > scikit-learn-request(a)python.org > > You can reach the person managing the list at > scikit-learn-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-learn digest..." > > Date: Tue, 10 Jan 2017 11:58:59 +0300 > From: avn(a)mccme.ru > To: Scikit-learn user and developer mailing list > <scikit-learn(a)python.org> > Subject: Re: [scikit-learn] Generalized Discriminant Analysis with > Kernel > Message-ID: <c2c15b0829e5facab0821dc078d90db1(a)mccme.ru> > Content-Type: text/plain; charset=UTF-8; format=flowed > > Hi Raga, > > You may try approximating your kernel using Nystroem kernel approximator > (kernel_approximation.Nystroem) and then apply LDA to the transformed > feature vectors. If you choose dimensionality of the target space > (n_components) large enough (depending on your kernel and data), > Nystroem approximator should provide sufficiently good kernel > approximation for such combination to approximate GDA. > > Raga Markely ????? 2017-01-09 19:29: > > Hello, > > > > I wonder if scikit-learn has implementation for generalized > > discriminant analysis using kernel approach? > > http://www.kernel-machines.org/papers/upload_21840_GDA.pdf > > > > I did some search, but couldn't find. > > > > Thank you, > > Raga > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn(a)python.org > > https://mail.python.org/mailman/listinfo/scikit-learn >

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