- 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.
- 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
- 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
- Switch to proper package name: python-scikit-learn
- Update to version 0.17
- Update to version 14.1
* Minor bugfixes
- Update to version 14.0
* Changelog
- Missing values with sparse and dense matrices can be imputed with the
transformer :class:`preprocessing.Imputer` by `Nicolas Trésegnie`_.
- The core implementation of decisions trees has been rewritten from
scratch, allowing for faster tree induction and lower memory
consumption in all tree-based estimators. By `Gilles Louppe`_.
- Added :class:`ensemble.AdaBoostClassifier` and
:class:`ensemble.AdaBoostRegressor`, by `Noel Dawe`_ and
`Gilles Louppe`_. See the :ref:`AdaBoost ` section of the user
guide for details and examples.
- Added :class:`grid_search.RandomizedSearchCV` and
:class:`grid_search.ParameterSampler` for randomized hyperparameter
optimization. By `Andreas Müller`_.
- Added :ref:`biclustering ` algorithms
(:class:`sklearn.cluster.bicluster.SpectralCoclustering` and
:class:`sklearn.cluster.bicluster.SpectralBiclustering`), data
generation methods (:func:`sklearn.datasets.make_biclusters` and
:func:`sklearn.datasets.make_checkerboard`), and scoring metrics
(:func:`sklearn.metrics.consensus_score`). By `Kemal Eren`_.
- Added :ref:`Restricted Boltzmann Machines`
(:class:`neural_network.BernoulliRBM`). By `Yann Dauphin`_.
- Python 3 support by `Justin Vincent`_, `Lars Buitinck`_,
`Subhodeep Moitra`_ and `Olivier Grisel`_. All tests now pass under
Python 3.3.
- Ability to pass one penalty (alpha value) per target in
:class:`linear_model.Ridge`, by @eickenberg and `Mathieu Blondel`_.
- Fixed :mod:`sklearn.linear_model.stochastic_gradient.py` L2 regularization
issue (minor practical significants).
By `Norbert Crombach`_ and `Mathieu Blondel`_ .
- Added an interactive version of `Andreas Müller`_'s
`Machine Learning Cheat Sheet (for scikit-learn)
`_
to the documentation. See :ref:`Choosing the right estimator `.
By `Jaques Grobler`_.
- :class:`grid_search.GridSearchCV` and
:func:`cross_validation.cross_val_score` now support the use of advanced
scoring function such as area under the ROC curve and f-beta scores.
See :ref:`scoring_parameter` for details. By `Andreas Müller`_
and `Lars Buitinck`_.
Passing a function from :mod:`sklearn.metrics` as ``score_func`` is
deprecated.
- Multi-label classification output is now supported by
:func:`metrics.accuracy_score`, :func:`metrics.zero_one_loss`,
:func:`metrics.f1_score`, :func:`metrics.fbeta_score`,
:func:`metrics.classification_report`,
:func:`metrics.precision_score` and :func:`metrics.recall_score`
by `Arnaud Joly`_.
- Two new metrics :func:`metrics.hamming_loss` and
:func:`metrics.jaccard_similarity_score`
are added with multi-label support by `Arnaud Joly`_.
- Speed and memory usage improvements in
:class:`feature_extraction.text.CountVectorizer` and
:class:`feature_extraction.text.TfidfVectorizer`,
by Jochen Wersdörfer and Roman Sinayev.
- The ``min_df`` parameter in
:class:`feature_extraction.text.CountVectorizer` and
:class:`feature_extraction.text.TfidfVectorizer`, which used to be 2,
has been reset to 1 to avoid unpleasant surprises (empty vocabularies)
for novice users who try it out on tiny document collections.
A value of at least 2 is still recommended for practical use.
- :class:`svm.LinearSVC`, :class:`linear_model.SGDClassifier` and
:class:`linear_model.SGDRegressor` now have a ``sparsify`` method that
converts their ``coef_`` into a sparse matrix, meaning stored models
trained using these estimators can be made much more compact.
- :class:`linear_model.SGDClassifier` now produces multiclass probability
estimates when trained under log loss or modified Huber loss.
- Hyperlinks to documentation in example code on the website by
`Martin Luessi`_.
- Fixed bug in :class:`preprocessing.MinMaxScaler` causing incorrect scaling
of the features for non-default ``feature_range`` settings. By `Andreas
Müller`_.
- ``max_features`` in :class:`tree.DecisionTreeClassifier`,
:class:`tree.DecisionTreeRegressor` and all derived ensemble estimators
now supports percentage values. By `Gilles Louppe`_.
- Performance improvements in :class:`isotonic.IsotonicRegression` by
`Nelle Varoquaux`_.
- :func:`metrics.accuracy_score` has an option normalize to return
the fraction or the number of correctly classified sample
by `Arnaud Joly`_.
- Added :func:`metrics.log_loss` that computes log loss, aka cross-entropy
loss. By Jochen Wersdörfer and `Lars Buitinck`_.
- A bug that caused :class:`ensemble.AdaBoostClassifier`'s to output
incorrect probabilities has been fixed.
- Feature selectors now share a mixin providing consistent `transform`,
`inverse_transform` and `get_support` methods. By `Joel Nothman`_.
- A fitted :class:`grid_search.GridSearchCV` or
:class:`grid_search.RandomizedSearchCV` can now generally be pickled.
By `Joel Nothman`_.
- Refactored and vectorized implementation of :func:`metrics.roc_curve`
and :func:`metrics.precision_recall_curve`. By `Joel Nothman`_.
- The new estimator :class:`sklearn.decomposition.TruncatedSVD`
performs dimensionality reduction using SVD on sparse matrices,
and can be used for latent semantic analysis (LSA).
By `Lars Buitinck`_.
- Added self-contained example of out-of-core learning on text data
:ref:`example_applications_plot_out_of_core_classification.py`.
By `Eustache Diemert`_.
- The default number of components for
:class:`sklearn.decomposition.RandomizedPCA` is now correctly documented
to be ``n_features``. This was the default behavior, so programs using it
will continue to work as they did.
- :class:`sklearn.cluster.KMeans` now fits several orders of magnitude
faster on sparse data (the speedup depends on the sparsity). By
`Lars Buitinck`_.
- Reduce memory footprint of FastICA by `Denis Engemann`_ and
`Alexandre Gramfort`_.
- Verbose output in :mod:`sklearn.ensemble.gradient_boosting` now uses
a column format and prints progress in decreasing frequency.
It also shows the remaining time. By `Peter Prettenhofer`_.
- :mod:`sklearn.ensemble.gradient_boosting` provides out-of-bag improvement
:attr:`~sklearn.ensemble.GradientBoostingRegressor.oob_improvement_`
rather than the OOB score for model selection. An example that shows
how to use OOB estimates to select the number of trees was added.
By `Peter Prettenhofer`_.
- Most metrics now support string labels for multiclass classification
by `Arnaud Joly`_ and `Lars Buitinck`_.
- New OrthogonalMatchingPursuitCV class by `Alexandre Gramfort`_
and `Vlad Niculae`_.
- Fixed a bug in :class:`sklearn.covariance.GraphLassoCV`: the
'alphas' parameter now works as expected when given a list of
values. By Philippe Gervais.
- Fixed an important bug in :class:`sklearn.covariance.GraphLassoCV`
that prevented all folds provided by a CV object to be used (only
the first 3 were used). When providing a CV object, execution
time may thus increase significantly compared to the previous
version (bug results are correct now). By Philippe Gervais.
- :class:`cross_validation.cross_val_score` and the :mod:`grid_search`
module is now tested with multi-output data by `Arnaud Joly`_.
- :func:`datasets.make_multilabel_classification` can now return
the output in label indicator multilabel format by `Arnaud Joly`_.
- K-nearest neighbors, :class:`neighbors.KNeighborsRegressor`
and :class:`neighbors.RadiusNeighborsRegressor`,
and radius neighbors, :class:`neighbors.RadiusNeighborsRegressor` and
:class:`neighbors.RadiusNeighborsClassifier` support multioutput data
by `Arnaud Joly`_.
- Random state in LibSVM-based estimators (:class:`svm.SVC`, :class:`NuSVC`,
:class:`OneClassSVM`, :class:`svm.SVR`, :class:`svm.NuSVR`) can now be
controlled. This is useful to ensure consistency in the probability
estimates for the classifiers trained with ``probability=True``. By
`Vlad Niculae`_.
- Out-of-core learning support for discrete naive Bayes classifiers
:class:`sklearn.naive_bayes.MultinomialNB` and
:class:`sklearn.naive_bayes.BernoulliNB` by adding the ``partial_fit``
method by `Olivier Grisel`_.
- New website design and navigation by `Gilles Louppe`_, `Nelle Varoquaux`_,
Vincent Michel and `Andreas Müller`_.
- Improved documentation on :ref:`multi-class, multi-label and multi-output
classification ` by `Yannick Schwartz`_ and `Arnaud Joly`_.
- Better input and error handling in the :mod:`metrics` module by
`Arnaud Joly`_ and `Joel Nothman`_.
- Speed optimization of the :mod:`hmm` module by `Mikhail Korobov`_
- Significant speed improvements for :class:`sklearn.cluster.DBSCAN`_
by `cleverless `_
* API changes:
- The :func:`auc_score` was renamed :func:`roc_auc_score`.
- Testing scikit-learn with `sklearn.test()` is deprecated. Use
`nosetest sklearn` from the command line.
- Feature importances in :class:`tree.DecisionTreeClassifier`,
:class:`tree.DecisionTreeRegressor` and all derived ensemble estimators
are now computed on the fly when accessing the ``feature_importances_``
attribute. Setting ``compute_importances=True`` is no longer required.
By `Gilles Louppe`_.
- :class:`linear_model.lasso_path` and
:class:`linear_model.enet_path` can return its results in the same
format as that of :class:`linear_model.lars_path`. This is done by
setting the `return_models` parameter to `False`. By
`Jaques Grobler`_ and `Alexandre Gramfort`_
- :class:`grid_search.IterGrid` was renamed to
:class:`grid_search.ParameterGrid`.
- Fixed bug in :class:`KFold` causing imperfect class balance in some
cases. By `Alexandre Gramfort`_ and Tadej Janež.
- :class:`sklearn.neighbors.BallTree` has been refactored, and a
:class:`sklearn.neighbors.KDTree` has been
added which shares the same interface. The Ball Tree now works with
a wide variety of distance metrics. Both classes have many new
methods, including single-tree and dual-tree queries, breadth-first
and depth-first searching, and more advanced queries such as
kernel density estimation and 2-point correlation functions.
By `Jake Vanderplas`_
- Support for scipy.spatial.cKDTree within neighbors queries has been
removed, and the functionality replaced with the new :class:`KDTree`
class.
- :class:`sklearn.neighbors.KernelDensity` has been added, which performs
efficient kernel density estimation with a variety of kernels.
- :class:`sklearn.decomposition.KernelPCA` now always returns output with
``n_components`` components, unless the new parameter ``remove_zero_eig``
is set to ``True``. This new behavior is consistent with the way
kernel PCA was always documented; previously, the removal of components
with zero eigenvalues was tacitly performed on all data.
- ``gcv_mode="auto"`` no longer tries to perform SVD on a densified
sparse matrix in :class:`sklearn.linear_model.RidgeCV`.
- Sparse matrix support in :class:`sklearn.decomposition.RandomizedPCA`
is now deprecated in favor of the new ``TruncatedSVD``.
- :class:`cross_validation.KFold` and
:class:`cross_validation.StratifiedKFold` now enforce `n_folds >= 2`
otherwise a ``ValueError`` is raised. By `Olivier Grisel`_.
- :func:`datasets.load_files`'s ``charset`` and ``charset_errors``
parameters were renamed ``encoding`` and ``decode_errors``.
- Attribute ``oob_score_`` in :class:`sklearn.ensemble.GradientBoostingRegressor`
and :class:`sklearn.ensemble.GradientBoostingClassifier`
is deprecated and has been replaced by ``oob_improvement_`` .
- Attributes in OrthogonalMatchingPursuit have been deprecated
(copy_X, Gram, ...) and precompute_gram renamed precompute
for consistency. See #2224.
- :class:`sklearn.preprocessing.StandardScaler` now converts integer input
to float, and raises a warning. Previously it rounded for dense integer
input.
- Better input validation, warning on unexpected shapes for y.
- Fix building on 13.1+
- Update BuildRequires
- Cleanup spec file formatting
- Require python-setuptools instead of distribute (upstreams merged)
- Update to version 0.13.1
- Update to version 0.12.1
- Clean up spec file
- Update to version 0.11
- remove unneeded libatals3-devel dependency
- fix python-Sphinx requirement
- first package
- version 0.5