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

imbalanced data
by S Hamidizade April 11, 2018

April 11, 2018

Hi Could you please let me know if the algorithms (including Robust Under-sampling, Cluster-Classify, MKL for high-class skew, ...) discussed in the following thesis have been implemented in Python? Robust Learning with Highly Skewed Category Distributions by Selen Uguroglu *https://pdfs.semanticscholar.org/c792/d83d78ff10b7a944d2c4e534c2c55bdf59b8.pdf <https://pdfs.semanticscholar.org/c792/d83d78ff10b7a944d2c4e534c2c55bdf59b8.…>* <https://www.google.com/search?source=hp&ei=WMLNWu2EBabn0gLOzLYo&q=Robust+Le…> Best regards,

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Contributing ST-DBSCAN to the clustering module
by Manmohit Rekhi April 10, 2018

April 10, 2018

Hi Everyone, This is my first open source contribution and I am really excited about scikit-learn I was reading a paper on ST-DBSCAN and found that there are no good implementations of it online. Thought this could a good opportunity to contribute. Had a few questions before I started 1) Is there any problem with this algorithm being in scikit-learn? 2) Is there someone already working on this algorithms? 3) Any things i need to take care of while contributing(like code optimisation/libraries to be used)? link to the paper: https://www.sciencedirect.com/science/article/pii/ S0169023X06000218 Thanks, Manmohit >

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Get parameters of classes in a Pipeline within cross_validate
by Roberto Guidotti April 7, 2018

April 7, 2018

Hi scikit-learners, I have a simple Pipeline with Feature Selection and SVC classifier and I use it in a cross validation schema with cross_validate / cross_validation_score functions. I need to extract the selected features for each fold of the CV and in general get information about the fitted elements of the pipeline in each of the CV fold. Is there a way to get these information (e.g. fs.get_support() or fs.scores_) or I need to build my own cross_validate function? Thank you, Roberto -- Ing. Roberto Guidotti, PhD. PostDoc Fellow Institute for Advanced Biomedical Technologies - ITAB Department of Neuroscience and Imaging University of Chieti "G. D'Annunzio" Via dei Vestini, 33 66013 Chieti, Italy tel: +39 0871 3556919 e-mail: r.guidotti(a)unich.it; rguidotti(a)acm.org linkedin: http://it.linkedin.com/in/robertogui/ twitter: @robbisg github: https://github.com/robbisg

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(no subject)
by PARK Jinwoo April 7, 2018

April 7, 2018

Dear scikit-learn experts Hello, I am a graduate school student majoring in doping control analysis in Korea. Now I'm in a research institute that carries out doping control analyses. I received a project by my advising doctor. It's about operating an AI project. A workshop is scheduled in April, so it needs to be done in a month. However, I haven't learn computer science at all and I'm totally ignorant of it. So I desperately need your advice. To be specific, the 3 xml files shown in the picture are analysis results named positive, negative, and unknown from top to bottom. We'd like to let AI learn positive and negative data, input unknown datum, and then see what result will turn out. I came to know that there's a module called 'iris calssification' in scikit-learn and I'm thinking of utilizing that as it seems similar with my assignment However, while the database of iris is a csv file with 150 data and labels inside, what I have are 3 xml files each one of which represents one data, which are stored in C:\Users\Jinwoo\Documents\Python Scripts\mzdata The training process is not shuffling randomly the 150 data and dividing into training set and test set. The data are already assigned into training ones and testing one. Also, when training the program, training labels naming positive and negative should be inserted on my own. What I know all is that it will be appropriate to use fit() function and predict() function to train and test. But I have no idea on what to import, how to write codes correctly, and so on It will be thankful to give me some help

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Outliers removal
by Alex Garel April 4, 2018

April 4, 2018

Hello, First, thanks for the fantastic scikit-learn library. I have the following use case: For a classification problem, I have a list of sentences and use word2vec and a method (eg. mean, or weigthed mean, or attention and mean) to transform sentences to vectors. Because my dataset is very noisy, I may come with sentences full of words that are not part of word2vec, hence I can't vectorize them. I would like to remove those sentences from my dataset X, but this would mean removing also the corresponding target classes in y. Afaik, scikit-learn does not implement this possibility. I've seen a couple of issues about that, but they all seems stalled : https://github.com/scikit-learn/scikit-learn/issues/9630, https://github.com/scikit-learn/scikit-learn/issues/3855, https://github.com/scikit-learn/scikit-learn/pull/4552, https://github.com/scikit-learn/scikit-learn/issues/4143 I would like to be able to search for hyper-parameters in a simple way, so I really would like to be able to use a single pipeline taking text as input. My actual conclusion is this one : * vectorizer should return None for bad samples (or a specific vector, like numpy.zeros, or add an extra column marking valid/invalid samples) * make all my transformers down the pipeline accept for those entries and leave them untouched (can be done with a generic wrapper class) * have a wrapper around my classifier, to avoid fitting on those, like jnothman suggested here https://github.com/scikit-learn/scikit-learn/issues/9630#issuecomment-32520… Its a bit tedious, but I can see it working. Is there any better suggestion ?

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NearestNeighbors without replacement
by Randy Ellis April 3, 2018

April 3, 2018

Hello to the Scikit-learn community! I am doing case-control matching for an electronic health records study. My question is, is it possible to run Sklearn's NearestNeighbors function without replacement? As in, match the treated group to the untreated group without re-using any of the untreated group data points? If so, how? By default, it uses replacement. I know this because I tested it on some data of mine. The code I used is in the confirmed answer here: https://stats.stackexchange.com/questions/206832/matched-pairs-in-python-pr… Thanks so much in advance, -- *Randall J. Ellis, B.S.* PhD Student, Biomedical Science, Mount Sinai Special Volunteer, http://www.michaelideslab.org/, NIDA IRP Cell: (954)-260-9891

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Error random_state parameter changed by estimator
by Manoj Karthick April 2, 2018

April 2, 2018

I am working on adding a new estimator to the scikit-learn library, but the make command always exits with the below error message: AssertionError: Estimator XYZ should not change or mutate the parameter random_state from 0 to <mtrand.RandomState object at 0x1a25da9e10> during fit. Can you help me understand what the issue is? Error log: self = <sklearn.utils._unittest_backport.TestCase testMethod=__init__> msg = ‘Estimator XYZ should not change or mutate the parameter random_state from 0 to <mtrand.RandomState object at 0x1a25da9e10> during fit.' def fail(self, msg=None): """Fail immediately, with the given message.""" > raise self.failureException(msg) E AssertionError: Estimator XYZ should not change or mutate the parameter random_state from 0 to <mtrand.RandomState object at 0x1a25da9e10> during fit. msg = 'Estimator XYZ should not change or mutate the parameter random_state from 0 to <mtrand.RandomState object at 0x1a25da9e10> during fit.' self = <sklearn.utils._unittest_backport.TestCase testMethod=__init__> Thanks in advance, Manoj Karthick Selva Kumar

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