Mailman 3 January 2020 - scikit-learn

ANN: scikit-learn 0.22 final release
by Adrin March 4, 2020

March 4, 2020

We're happy to announce the 0.22 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_relea… and the long version of the change log under https://scikit-learn.org/stable/whats_new/v0.22.html#changes-0-22. This version supports Python versions 3.5 to 3.8. You can give it a go using `pip install -U scikit-learn` while conda and conda forge binaries are coming. Regards, Adrin, on behalf of the scikit-learn maintainer team.

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make_spd_matrix documentation
by Sina Mansour L. Jan. 31, 2020

Jan. 31, 2020

Hi, I was trying to use the random positive definite matrix generator implemented in sklearn ( https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_spd…). However, I noticed the documentation is very minimal, without any references on related research articles. I'm wondering if it generates a uniformly random covariance matrix to be used as a sampling method to generate a null distribution for covariances. I tried looking at the source code. But I didn't find any reasons or explanations explaining the method. In other words, I wanted to know if the random symmetric positive definite matrix returned is uniformly sampled from the space of all positive definite matrices or not. This is the idea I was interested in when I came across make_spd_matrix: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.594.3009&rep=rep1&… Kind regards, Sina

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SLEP011: Change of governance
by Guillaume Lemaître Jan. 30, 2020

Jan. 30, 2020

Dear all, I would like to propose a change of governance in the decision process to make it possible to retract a SLEP and to not escalate a mandatory TC vote. I open a SLEP where we can discuss about it: https://github.com/scikit-learn/enhancement_proposals/pull/28/files Cheers, -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/

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Which sparse matrix should be use for fit?
by Peng Yu Jan. 29, 2020

Jan. 29, 2020

https://scikit-learn.org/stable/modules/svm.html Of the svm classes mentioned above, which sparse matrixes are appropriate to be used with them? https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matri… It is not very clear what matrix operations are used in fit(), so I can not tell what sparse matrixes should be used. Thanks. -- Regards, Peng

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Incremental generation of tf-idf matrix
by Peng Yu Jan. 29, 2020

Jan. 29, 2020

Hi, I seem that even if there is a slight change in the corpus, I have to run TfidfVectorizer on the whole corpus again. This can be time-consuming especially for large corpora. Is there a way to generate the tf-idf matrix incrementally so that if there is a slight change in the corpus, it will just take a little time instead of a lot of time to generate the tf-idf matrix? Thanks. -- Regards, Peng

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How to make sure stop words are matched when lowercase=False?
by Peng Yu Jan. 28, 2020

Jan. 28, 2020

Hi, https://github.com/scikit-learn/scikit-learn/blob/002f891a33b612be389d9c488… The default of lowercase is True. But stopwords are lower case. Where is the code to make sure the stop words are removed when they are not in lower case? https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_ex… -- Regards, Peng

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Recommended way of distributing persisted models so they work on different architectures
by Christopher.samiullah Jan. 28, 2020

Jan. 28, 2020

Dear admins, > I recently encountered an issue attempting to load a model persisted via joblib dump on different Python architectures. I wrote up the issue here on stackoverflow: > https://stackoverflow.com/questions/59927368/how-to-distribute-sklearn-mode… > > I wondered if there was a recommended approach to mitigate this issue? I see there is sklearn-onyx (https://github.com/onnx/sklearn-onnx) is that something you would advise? > > Any help would be greatly appreciated. > > Kind regards, > Chris p.s. Apologies if this is a resend, I wasn't signed up to the list when I sent previously. > Sent with [ProtonMail](https://protonmail.com) Secure Email.

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Memory efficient TfidfVectorizer
by Peng Yu Jan. 28, 2020

Jan. 28, 2020

Hi, To use TfidfVectorizer, the whole corpus must be used into memory. This can be a problem for machines without a lot of memory. Is there a way to use only a small amount of memory by saving most intermediate results in the disk? Thanks. -- Regards, Peng

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Why is subset invariance necessary for transfom()?
by Charles Pehlivanian Jan. 27, 2020

Jan. 27, 2020

I understand - I'm kind of conflating the idea of data sample with test set, my view assumes there are a sample space of samples, might require rethinking the cross-validation setup... I also think that part of it relies on the notion of online vs. offline algorithm. For offline fits, a batch transform (non-subset invariant) is preferred. For a transformer that can only be used in an online sense, or is primarily used that way, keep the invariant. I see 3 options here - all I can say is that I don't vote for the first + No transform method on the manifold learners, so no cross-validation + Pointwise, distributable, subset-invariant, suboptimal greedy transform + Non-distributable, non-subset-invariant, optimal batch transform -Charles On Mon., Jan. 20, 21:24:52 2020 <joel.nothman at gmail.com <scikit-learn%40python.org?Subject=Re%3A%20%5Bscikit-learn%5D%20Why%20is%20subset%20invariance%20necessary%20for%0A%20transfom%28%29%3F&In-Reply-To=%3CCAAkaFLWfWyu%2BDdQ3RX5tBays6jLX6A3W_QpqAcWn_RAxbRz5cQ%40mail.gmail.com%3E>> wrote I think allowing subset invariance to not hold is making stronger assumptions than we usually do about what it means to have a "test set". Having a transformation like this that relies on test set statistics implies that the test set is more than just selected samples, but rather that a large collection of samples is available at one time, and that it is in some sense sufficient or complete (no more samples are available that would give a better fit). So in a predictive modelling context you might have to set up your cross validation splits with this in mind. In terms of API, the subset invariance constraint allows us to assume that the transformation can be distributed or parallelized over samples. I'm not sure whether we have exploited that assumption within scikit-learn or whether related projects do so. I see the benefit of using such transformations in a prediction Pipeline, and really appreciate this challenge to our assumptions of what "transform" means. Joel On Tue., 21 Jan. 2020, 11:50 am Charles Pehlivanian, <pehlivaniancharles at gmail.com <https://mail.python.org/mailman/listinfo/scikit-learn>> wrote: >* Not all data transformers have a transform method. For those that do, *>* subset invariance is assumed as expressed *>* in check_methods_subset_invariance(). It must be the case that *>* T.transform(X)[i] == T.transform(X[i:i+1]), e.g. This is true for classic *>* projections - PCA, kernel PCA, etc., but not for some manifold learning *>* transformers - MDS, SpectralEmbedding, etc. For those, an optimal placement *>* of the data in space is a constrained optimization, may take into account *>* the centroid of the dataset etc. *>>* The manifold learners have "batch" oos transform() methods that aren't *>* implemented, and wouldn't pass that test. Instead, those that do - *>* LocallyLinearEmbedding - use a pointwise version, essentially replacing a *>* batch fit with a suboptimal greedy one [for LocallyLinearEmbedding]: *>>* for i in range(X.shape[0]): *>* X_new[i] = np.dot(self.embedding_[ind[i]].T, weights[i]) *>>* Where to implement the batch transform() methods for MDS, *>* SpectralEmbedding, LocallyLinearEmbedding, etc? *>>* Another verb? Both batch and pointwise versions? The latter is easy to *>* implement once the batch version exists. Relax the test conditions? *>* transform() is necessary for oos testing, so necessary for cross *>* validation. The batch versions should be preferred, although as it stands, *>* the pointwise versions are. *>>* Thanks *>* Charles Pehlivanian *>* _______________________________________________ *>* scikit-learn mailing list *>* scikit-learn at python.org <https://mail.python.org/mailman/listinfo/scikit-learn> *>* https://mail.python.org/mailman/listinfo/scikit-learn <https://mail.python.org/mailman/listinfo/scikit-learn> *>

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Assigning reviewers to PRs
by Madhura Jayaratne Jan. 23, 2020

Jan. 23, 2020

Hi Scikit-learn team, I have submitted a PR implementing support for ICE plots at [1]. There I noticed that Github suggests reviewers based on the recent edits and reviews to the files changed in the PR. I am wondering what is the preferred practice for assigning reviewers. (Initially, I assigned Guillaume Lemaitre without thinking much, but he cannot review at the moment) I am wondering, should I assign someone from the suggested list or should I wait for the core developers to assign themselves? [1] https://github.com/scikit-learn/scikit-learn/pull/16164 -- Thanks and Regards, Madhura Jayaratne

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