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python2-scikit-learn-0.20.2-lp152.1.3 RPM for x86_64

From OpenSuSE Leap 15.2 for x86_64

Name: python2-scikit-learn Distribution: openSUSE Leap 15.2
Version: 0.20.2 Vendor: openSUSE
Release: lp152.1.3 Build date: Sat May 16 19:35:38 2020
Group: Development/Libraries/Python Build host: wildcard2
Size: 24657099 Source RPM: python2-scikit-learn-0.20.2-lp152.1.3.src.rpm
Packager: https://bugs.opensuse.org
Url: http://scikit-learn.org/
Summary: Python modules for machine learning and data mining
Scikit-learn is a python module for machine learning built on top of
scipy.

Provides

Requires

License

BSD-3-Clause

Changelog

* Thu Jul 25 2019 Todd R <toddrme2178@gmail.com>
  - Implement python2-only version since the latest release
    drops python2 support.
* Wed Jan 30 2019 Matej Cepl <mcepl@suse.com>
  - Switch off tests, gh#scikit-learn/scikit-learn#12369
* Tue Jan 29 2019 mcepl@suse.com
  - Update to 0.20.2:
    * This is a bug-fix release with some minor documentation
      improvements and enhancements to features released in 0.20.0.
      Note that we also include some API changes in this release, so
      you might get some extra warnings after updating from 0.20.0.
* Wed Oct 24 2018 Dirk Mueller <dmueller@suse.com>
  - update to 0.20.0:
    * http://scikit-learn.org/stable/whats_new.html#version-0-20-0
    Support for Python 3.3 has been officially dropped
  - drop scikit-learn-skip-test.patch (merged)
* Thu May 17 2018 tchvatal@suse.com
  - Skip test sklearn.linear_model.tests.test_logistic.test_max_iter
    * Upstream plans to fix it in next release
    * scikit-learn-skip-test.patch
* Thu May 17 2018 tchvatal@suse.com
  - Update package to properly state dependencies as in setup.py
  - Install license file
* Mon Oct 30 2017 arun@gmx.de
  - update to version 0.19.1:
    * API changes
      + Reverted the addition of metrics.ndcg_score and
      metrics.dcg_score which had been merged into version 0.19.0 by
      error. The implementations were broken and undocumented.
      + return_train_score which was added to
      model_selection.GridSearchCV, model_selection.RandomizedSearchCV
      and model_selection.cross_validate in version 0.19.0 will be
      changing its default value from True to False in version
      0.21. We found that calculating training score could have a
      great effect on cross validation runtime in some cases. Users
      should explicitly set return_train_score to False if prediction
      or scoring functions are slow, resulting in a deleterious effect
      on CV runtime, or to True if they wish to use the calculated
      scores. #9677 by Kumar Ashutosh and Joel Nothman.
      + correlation_models and regression_models from the legacy
      gaussian processes implementation have been belatedly
      deprecated. #9717 by Kumar Ashutosh.
    * Bug fixes
      + Avoid integer overflows in metrics.matthews_corrcoef. #9693 by
      Sam Steingold.
      + Fix ValueError in preprocessing.LabelEncoder when using
      inverse_transform on unseen labels. #9816 by Charlie Newey.
      + Fixed a bug in the objective function for manifold.TSNE (both
      exact and with the Barnes-Hut approximation) when n_components
      >= 3. #9711 by @goncalo-rodrigues.
      + Fix regression in model_selection.cross_val_predict where it
      raised an error with method='predict_proba' for some
      probabilistic classifiers. #9641 by James Bourbeau.
      + Fixed a bug where datasets.make_classification modified its
      input weights. #9865 by Sachin Kelkar.
      + model_selection.StratifiedShuffleSplit now works with
      multioutput multiclass or multilabel data with more than 1000
      columns. #9922 by Charlie Brummitt.
      + Fixed a bug with nested and conditional parameter setting,
      e.g. setting a pipeline step and its parameter at the same
      time. #9945 by Andreas Müller and Joel Nothman.
    * Regressions in 0.19.0 fixed in 0.19.1:
      + Fixed a bug where parallelised prediction in random forests was
      not thread-safe and could (rarely) result in arbitrary
      errors. #9830 by Joel Nothman.
      + Fix regression in model_selection.cross_val_predict where it no
      longer accepted X as a list. #9600 by Rasul Kerimov.
      + Fixed handling of model_selection.cross_val_predict for binary
      classification with method='decision_function'. #9593 by
      Reiichiro Nakano and core devs.
      + Fix regression in pipeline.Pipeline where it no longer accepted
      steps as a tuple. #9604 by Joris Van den Bossche.
      + Fix bug where n_iter was not properly deprecated, leaving n_iter
      unavailable for interim use in linear_model.SGDClassifier,
      linear_model.SGDRegressor,
      linear_model.PassiveAggressiveClassifier,
      linear_model.PassiveAggressiveRegressor and
      linear_model.Perceptron. #9558 by Andreas Müller.
      + Dataset fetchers make sure temporary files are closed before
      removing them, which caused errors on Windows. #9847 by Joan
      Massich.
      + Fixed a regression in manifold.TSNE where it no longer supported
      metrics other than ‘euclidean’ and ‘precomputed’. #9623 by Oli
      Blum.
    * Enhancements
      + Our test suite and utils.estimator_checks.check_estimators can
      now be run without Nose installed. #9697 by Joan Massich.
      + To improve usability of version 0.19’s pipeline.Pipeline
      caching, memory now allows joblib.Memory instances. This make
      use of the new utils.validation.check_memory helper. #9584 by
      Kumar Ashutosh
      + Some fixes to examples: #9750, #9788, #9815
      + Made a FutureWarning in SGD-based estimators less verbose. #9802
      by Vrishank Bhardwaj.
* Sun Sep 24 2017 arun@gmx.de
  - update to version 0.19.0:
    * Highlights
      + We are excited to release a number of great new features
      including neighbors.LocalOutlierFactor for anomaly detection,
      preprocessing.QuantileTransformer for robust feature
      transformation, and the multioutput.ClassifierChain
      meta-estimator to simply account for dependencies between
      classes in multilabel problems. We have some new algorithms in
      existing estimators, such as multiplicative update in
      decomposition.NMF and multinomial
      linear_model.LogisticRegression with L1 loss (use
      solver='saga').
      + Cross validation is now able to return the results from multiple
      metric evaluations. The new model_selection.cross_validate can
      return many scores on the test data as well as training set
      performance and timings, and we have extended the scoring and
      refit parameters for grid/randomized search to handle multiple
      metrics.
      + You can also learn faster. For instance, the new option to cache
      transformations in pipeline.Pipeline makes grid search over
      pipelines including slow transformations much more
      efficient. And you can predict faster: if you’re sure you know
      what you’re doing, you can turn off validating that the input is
      finite using config_context.
      + We’ve made some important fixes too. We’ve fixed a longstanding
      implementation error in metrics.average_precision_score, so
      please be cautious with prior results reported from that
      function. A number of errors in the manifold.TSNE implementation
      have been fixed, particularly in the default Barnes-Hut
      approximation. semi_supervised.LabelSpreading and
      semi_supervised.LabelPropagation have had substantial
      fixes. LabelPropagation was previously broken. LabelSpreading
      should now correctly respect its alpha parameter.
    * Changed models
    * The following estimators and functions, when fit with the same
      data and parameters, may produce different models from the
      previous version. This often occurs due to changes in the
      modelling logic (bug fixes or enhancements), or in random sampling
      procedures.
      + cluster.KMeans with sparse X and initial centroids given (bug
      fix)
      + cross_decomposition.PLSRegression with scale=True (bug fix)
      + ensemble.GradientBoostingClassifier and
      ensemble.GradientBoostingRegressor where min_impurity_split is
      used (bug fix)
      + gradient boosting loss='quantile' (bug fix)
      + ensemble.IsolationForest (bug fix)
      + feature_selection.SelectFdr (bug fix)
      + linear_model.RANSACRegressor (bug fix)
      + linear_model.LassoLars (bug fix)
      + linear_model.LassoLarsIC (bug fix)
      + manifold.TSNE (bug fix)
      + neighbors.NearestCentroid (bug fix)
      + semi_supervised.LabelSpreading (bug fix)
      + semi_supervised.LabelPropagation (bug fix)
      + tree based models where min_weight_fraction_leaf is used
      (enhancement)
    * complete changelog at http://scikit-learn.org/stable/whats_new.html
* Sun Jun 11 2017 toddrme2178@gmail.com
  - Implement single-spec version
  - Update source URL
  - Update to version 0.18.1
    * Large number of changes. See:
      https://github.com/scikit-learn/scikit-learn/blob/0.18.1/doc/whats_new.rst
* Mon Jan 11 2016 toddrme2178@gmail.com
  - Switch to proper package name: python-scikit-learn
* Fri Nov 20 2015 Angelos Tzotsos <tzotsos@opensuse.org>
  - Update to version 0.17

Files

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Fabrice Bellet, Sat Mar 9 11:51:54 2024