Mailman 3 December 2019 - 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.

5 6

Heisenbug?
by Dan Stromberg Jan. 17, 2020

Jan. 17, 2020

Hi folks. I'm new to Scikit-learn. I have a very large Python project that seems to have a heisenbug which is manifesting in scikit-learn code. Short of constructing an SSCCE, are there any magical techniques I should try for pinning down the precise cause? Like valgrind or something? An SSCCE will most likely be pretty painful: the project has copious shared, mutable state, and I've already tried a largish test program that calls into the same code path with the error manifesting 0 times in 100. It's quite possible the root cause will turn out to be some other part of the software stack. The traceback from pytest looks like: sequential/test_training.py:101: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../rt/classifier/coach.py:146: in train **self.classifier_section ../domain/classifier/factories/classifier_academy.py:115: in create_classifier **kwargs) ../domain/classifier/factories/imp/xgb_factory.py:164: in create clf_random.fit(X_train, y_train) ../../../../.local/lib/python3.6/site-packages/sklearn/model_selection/_search.py:722: in fit self._run_search(evaluate_candidates) ../../../../.local/lib/python3.6/site-packages/sklearn/model_selection/_search.py:1515: in _run_search random_state=self.random_state)) ../../../../.local/lib/python3.6/site-packages/sklearn/model_selection/_search.py:711: in evaluate_candidates cv.split(X, y, groups))) ../../../../.local/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py:996: in __call__ self.retrieve() ../../../../.local/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py:899: in retrieve self._output.extend(job.get(timeout=self.timeout)) ../../../../.local/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py:517: in wrap_future_result return future.result(timeout=timeout) /usr/lib/python3.6/concurrent/futures/_base.py:425: in result return self.__get_result() _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <Future at 0x7f15571ec7f0 state=finished raised ValueError> def __get_result(self): if self._exception: > raise self._exception E ValueError: Input contains NaN, infinity or a value too large for dtype('float32'). /usr/lib/python3.6/concurrent/futures/_base.py:384: ValueError The above exception is raised about 12 to 14 times in 100 in full-blown automated testing. Thanks for the cool software.

2 5

logistic regression results are not stable between solvers
by Benoît Presles Jan. 9, 2020

Jan. 9, 2020

Dear scikit-learn users, I am using logistic regression to make some predictions. On my own data, I do not get the same results between solvers. I managed to reproduce this issue on synthetic data (see the code below). All solvers seem to converge (n_iter_ < max_iter), so why do I get different results? If results between solvers are not stable, which one to choose? Best regards, Ben ------------------------------------------ Here is the code I used to generate synthetic data: from sklearn.datasets import make_classification from sklearn.model_selection import StratifiedShuffleSplit from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression # RANDOM_SEED = 2 # X_sim, y_sim = make_classification(n_samples=200, n_features=45, n_informative=10, n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=1, random_state=RANDOM_SEED, shuffle=False) # sss = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=RANDOM_SEED) for train_index_split, test_index_split in sss.split(X_sim, y_sim): X_split_train, X_split_test = X_sim[train_index_split], X_sim[test_index_split] y_split_train, y_split_test = y_sim[train_index_split], y_sim[test_index_split] ss = StandardScaler() X_split_train = ss.fit_transform(X_split_train) X_split_test = ss.transform(X_split_test) # classifier_lbfgs = LogisticRegression(fit_intercept=True, max_iter=20000000, verbose=1, random_state=RANDOM_SEED, C=1e9, solver='lbfgs') classifier_lbfgs.fit(X_split_train, y_split_train) print('classifier lbfgs iter:', classifier_lbfgs.n_iter_) classifier_saga = LogisticRegression(fit_intercept=True, max_iter=20000000, verbose=1, random_state=RANDOM_SEED, C=1e9, solver='saga') classifier_saga.fit(X_split_train, y_split_train) print('classifier saga iter:', classifier_saga.n_iter_) # y_pred_lbfgs = classifier_lbfgs.predict(X_split_test) y_pred_saga = classifier_saga.predict(X_split_test) # if (y_pred_lbfgs==y_pred_saga).all() == False: print('lbfgs does not give the same results as saga :-( !') exit()

5 17

Vote on SLEP010: n_features_in_ attribute
by Nicolas Hug Jan. 6, 2020

Jan. 6, 2020

As per our Governance <http://scikit-learn.org/stable/governance.html> document, changes to API principles are to be established through an Enhancement Proposal (SLEP) from which any core developer can call for a vote on its acceptance. * * *SLEP010: n_features_in attribute *is up for a vote. Please see https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep010… * * * *This SLEP proposes the introduction of a public n_features_in_ attribute for most estimators* Core developers are invited to vote on this change until 4 January 2020 by replying to this email thread. All members of the community are welcome to comment on the proposal on this mailing list, or to propose minor changes through Issues and Pull Requests at https://github.com/scikit-learn/enhancement_proposals/.

13 22

Calculate cohen_kappa_score class wise?
by Pavel G. Dec. 18, 2019

Dec. 18, 2019

Hi sklearn developers ans users, The cohen_kappa_score calculates overall score for all classes. y_true = [0, 1, 2, 2, 2] y_pred = [0, 0, 2, 2, 1] target_names = ['class 0', 'class 1', 'class 2'] from sklearn.metrics import cohen_kappa_score print (cohen_kappa_score(y_true, y_pred)) 0.375 How can I calculate cohen_kappa_score class wise, like the classification_report does. I hope someone can help me. Thank you. Pavel G.

1 0

Paris Sprint in January and wiki update
by Chiara Marmo Dec. 17, 2019

Dec. 17, 2019

Dear all, as promised I have published a page about the January Sprint in Paris on the scikit-learn wiki [1] More details will be added but the link to the registration form and a preliminary programme are already there. Is that worthing a Tweet? I mean a scikit_learn tweet... At the same time an "Upcoming events" page is now available [2] : I have linked there the WiLMDS sprint in Berlin... this is the only future event of which I'm aware of... Is this page worthing a tweet too? :) Thanks for listening, Best, Chiara [1] https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint… [2] https://github.com/scikit-learn/scikit-learn/wiki/Upcoming-events

3 2

ANN: imbalanced-learn 0.6 released
by Christos Aridas Dec. 9, 2019

Dec. 9, 2019

Hi all, We're happy to announce the 0.6 (and 0.6.1) release. imbalanced-learn is a toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced data sets frequently encountered in machine learning and pattern recognition. In this release several enhancements, bug fixes and maintenance tasks have been introduced. Notable enhancements: 1) Pandas DataFrame in, Pandas DataFrame out. 2) Speed-up of the resampling procedure in some samplers due to internal vectorization. The full changelog can be found here: http://imbalanced-learn.org/stable/whats_new.html The new release of imbalanced-learn is already available via pip and conda! For more information, examples, and documentation, please visit our website: http://imbalanced-learn.org Cheers, Chris, on behalf of the imbalanced-learn team.

1 0

Update of 'Upcoming events' on the scikit-learn wiki
by Chiara Marmo Dec. 6, 2019

Dec. 6, 2019

Dear core-devs, I would like to advertise about our Paris sprint (end of January) on the scikit-learn wiki. If there are no objections, my goal is to make a list of events in the 'Upcoming events' page [1] and link the event pages from there. This will allow to link also events organised by other entities (like WiMLDS) even if pages are not hosted there. Please, let me know if you all are ok with that. Thanks for listening, best Chiara [1] https://github.com/scikit-learn/scikit-learn/wiki/Upcoming-events

2 1

Version 0.21! and plot_tree!
by Andrew Howe Dec. 5, 2019

Dec. 5, 2019

I want to say thank you to all the sklearn developers. The breadth and quality of this software is truly breathtaking. Specifically, I want to say thank you very very much for the plot_tree function! I have wasted a lot of effort in the past, on multiple OSes, getting everything to work so I could view the tree.export_graphviz results. Having this new function to plot the trees natively in matplotlib is extremely useful. Thanks again! Andrew <~~~~~~~~~~~~~~~~~~~~~~~~~~~> J. Andrew Howe, PhD LinkedIn Profile <http://www.linkedin.com/in/ahowe42> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/> Open Researcher and Contributor ID (ORCID) <http://orcid.org/0000-0002-3553-1990> Github Profile <http://github.com/ahowe42> Personal Website <http://www.andrewhowe.com> I live to learn, so I can learn to live. - me <~~~~~~~~~~~~~~~~~~~~~~~~~~~>

2 4

SVM-RFE
by Malik Yousef Dec. 5, 2019

Dec. 5, 2019

Hello, Let's assume that I have data with 1000 features. I want to apply SVM-RFE on this data where each time 10% for the features are removed. How one can get the accuracy overall the levels of the elimination stages. For example, I want to get performance over 1000 features, 900 features, 800 features,....,2 features, 1 feature. Also, I want to keep track of the features in each level. [image: image.png] Best --------------------------------------------------------------------------------------- *Prof. Malik Yousef (Associate Professor) * *The Head of the** Galilee Digital Health Research Center (GDH)* *Zefat Academic College , Department of Information System * Home Page: https://malikyousef.com/ Google Scholar Profile : https://scholar.google.com/citations?user=9UCZ_q4AAAAJ&hl=en&oi=ao ----------------------------------------------------------------------------------------------------

3 8