Mailman 3 July 2018 - scikit-learn

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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Query about an algorithm
by Shantanu Bhattacharya Aug. 1, 2018

Aug. 1, 2018

Hello, I am new to this mailing list. I would like to understand the algorithms provided. Is second order gradient descent with hessian error matrix supported by this library? I went through the documentation, but did not find it. Are you able to confirm or direct me to some place that might have it? Look forward to your thoughts Kind regardsShantanu

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Dependency issues
by Prathusha Jonnagaddla Subramanyam Naidu July 31, 2018

July 31, 2018

Hi everyone, I updated the version of SCIKIT_IMAGE used in circle build to 0.14.0. This is the error that I got UnsatisfiableError: The following specifications were found to be in conflict: - numpy=1.8.2 - scikit-image=0.14.0 -> scipy[version='>=0.17']Use "conda info <package>" to see the dependencies for each package. So I updated scipy version to 1.1.0 and I get this error now UnsatisfiableError: The following specifications were found to be in conflict: - pandas=0.13.1 - scipy=1.1.0Use "conda info <package>" to see the dependencies for each package. Should I update the versions of everything ? Or am I doing something wrong ? -- Regards, Prathusha JS Naidu

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Suggestion to update the code for Segmenting the picture of Lena in regions
by Rajkiran Veldur July 29, 2018

July 29, 2018

Hello Team, I have been following scikit-learn closely these days as I have been working on different machine learning algorithms. Thank you for making everything so simple. Your documents could be followed even by novice. Now, when I was working with spectral clustering, I found your example of *Segmenting the picture of Lena in regions *intuitive and wanted to try it. However, scipy has removed the scipy.misc.lena() module from their library, due to licensing issues. So, I request you to please update the code with any other image. Regards, Rajkiran Veldur

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f_classif function confusion
by Trenton Bricken July 26, 2018

July 26, 2018

I am very confused by the f_classif function using feature.selection.f_classif() The function under the User Guide says that it can be used for classification tasks and under the documentation it claims to use ANOVA. However, ANOVA takes a categorical input and continuous output. Why can we provide it with a continuous input and categorical output here? Also looking at the source code, there are warnings for not using ANOVA if your feature is not normally distributed. I think these should be more visible to warn the unaware before they start using this method for feature analysis. Thank you in advance for any help and explanations that can be provided about why f_classif can be used with categorical outputs. Trenton

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What is the FeatureAgglomeration algorithm?
by Raphael C July 26, 2018

July 26, 2018

Hi, I am trying to work out what, in precise mathematical terms, [FeatureAgglomeration][1] does and would love some help. Here is some example code: import numpy as np from sklearn.cluster import FeatureAgglomeration for S in ['ward', 'average', 'complete']: FA = FeatureAgglomeration(linkage=S) print(FA.fit_transform(np.array([[-50,6,6,7,], [0,1,2,3]]))) This outputs: [[ 6.33333333 -50. ] [ 2. 0. ]] [[ 6.33333333 -50. ] [ 2. 0. ]] [[ 6.33333333 -50. ] [ 2. 0. ]] Is it possible to say mathematically how these values have been computed? Also, what exactly does linkage do and why doesn't it seem to make any difference which option you choose? Raphael [1]: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.FeatureAgg… PS I also asked at https://stackoverflow.com/questions/51526616/what-does-featureagglomeration…

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RFE with logistic regression
by Benoît Presles July 25, 2018

July 25, 2018

Dear scikit-learn users, I am using the recursive feature elimination (RFE) tool from sklearn to rank my features: from sklearn.linear_model import LogisticRegression classifier_RFE = LogisticRegression(C=1e9, verbose=1, max_iter=10000) from sklearn.feature_selection import RFE rfe = RFE(estimator=classifier_RFE, n_features_to_select=1, step=1) rfe.fit(X, y) ranking = rfe.ranking_ print(ranking) 1. The first problem I have is when I execute the above code multiple times, I don't get the same results. 2. When I change the solver to 'sag' or 'saga' (classifier_RFE = LogisticRegression(C=1e9, verbose=1, max_iter=10000), solver='sag'), it seems that I get the same results at each run but the ranking is not the same between these two solvers. 3. With C=1, it seems I have the same results at each run for the solver='liblinear', but not for the solvers 'sag' and 'saga'. I still don't get the same results between the different solvers. Thanks for your help, Best regards, Ben

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Would love to contribute to this library that I fell in love with. I have a question! FIRST TIMER
by Abhishek Babuji July 25, 2018

July 25, 2018

TO WHOM IT MAY CONCERN, I have just learned Python to a level that I can say I'm comfortable with it. I have also picked up and learned Git and GitHub, and so now I'm ready to make my contribution to this library. I'm really enthusiastic but since this is my first time, I'd like to know a few things! *Must I know the underlying implementation of something to contribute code to fix it?* Explanation: Let's say, someone, tags some issue as 'first timers' and 'easy', and you want to take a look at it, see and contribute code/fix the code. Should I know the implementation of what the fixed code is supposed to do? or will this be explained when the issue is brought up? I have gone over issues in your GitHub. but I don't think I've seen enough examples. I don't seem to find this in the contributor guide. If someone could help me understand the level of depth that I must know scikit-learn to be able to contribute, I would then begin working towards it! Because I have used it a lot in my Machine Learning projects, so I'm not sure where I stand. Example: "The shovel doesn't work! Fix it! It is supposed to be able to dig through mud" My dilemma: I found an immovable rock in the mud that the shovel is not being able to dig through.. so I'm stuck. Guess I shouldn't have volunteered to help. Just on a side note, to all scikit-learn's contributors, you're doing God's work.

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Help with Pull Request( Checks failing)
by Prathusha Jonnagaddla Subramanyam Naidu July 25, 2018

July 25, 2018

Hi everyone, I submitted my first PR few hours back and I see that two tests failed. Would really appreciate if anyone can help me with how to fix these/ what I am doing wrong. Thank you !

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Re: [scikit-learn] Plot Cross-validated ROCs for multi-class classification problem
by Andreas Mueller July 21, 2018

July 21, 2018

Please stay on the mailing list. There is no single roc curve for a 3 class problem. So what do you want to plot? On 07/20/2018 10:40 AM, serafim loukas wrote: > Hello Andy, > > > Thank you for your response. > > What I want to do is to plot the average(mean) ROC across Folds for a > 3-class case. > I have managed to do so for the binary case and I am trying to make it > work for the mutlti-class case but with no luck. > > There is an example in the documentation > (http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_cross…) > for the binary case. > I want to do the same ( plot the mean ROC and the confidence interval > for my 3-class problem). > > Here is also my SO question about this: > https://stackoverflow.com/questions/51442818/average-roc-curve-across-folds… with > some code included. > > > > Best, > Makis > > > >> On 20 Jul 2018, at 16:34, Andreas Mueller <t3kcit(a)gmail.com >> <mailto:t3kcit@gmail.com>> wrote: >> >> Hi Makis. >> What do you mean by a roc curve for multi-class? >> You can have one curve per class using OVR or one curve per pair of >> classes. >> That doesn't need the OneVsRestClassifier, it's more a matter of >> evaluation. >> >> Cheers, >> Andy >> >> On 07/20/2018 05:35 AM, serafim loukas wrote: >>> 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 >>> >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn(a)python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >> >

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