.. include:: _contributors.rst
.. currentmodule:: sklearn
.. _changes_0_21_3:
Version 0.21.3
==============
.. include:: changelog_legend.inc
**July 30, 2019**
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.
- The v0.20.0 release notes failed to mention a backwards incompatibility in
:func:`metrics.make_scorer` when `needs_proba=True` and `y_true` is binary.
Now, the scorer function is supposed to accept a 1D `y_pred` (i.e.,
probability of the positive class, shape `(n_samples,)`), instead of a 2D
`y_pred` (i.e., shape `(n_samples, 2)`).
Changelog
---------
:mod:`sklearn.cluster`
......................
- |Fix| Fixed a bug in :class:`cluster.KMeans` where computation with
`init='random'` was single threaded for `n_jobs > 1` or `n_jobs = -1`.
:pr:`12955` by :user:`Prabakaran Kumaresshan `.
- |Fix| Fixed a bug in :class:`cluster.OPTICS` where users were unable to pass
float `min_samples` and `min_cluster_size`. :pr:`14496` by
:user:`Fabian Klopfer `
and :user:`Hanmin Qin `.
- |Fix| Fixed a bug in :class:`cluster.KMeans` where KMeans++ initialisation
could rarely result in an IndexError. :issue:`11756` by `Joel Nothman`_.
:mod:`sklearn.compose`
......................
- |Fix| Fixed an issue in :class:`compose.ColumnTransformer` where using
DataFrames whose column order differs between :func:``fit`` and
:func:``transform`` could lead to silently passing incorrect columns to the
``remainder`` transformer.
:pr:`14237` by `Andreas Schuderer `.
:mod:`sklearn.datasets`
.......................
- |Fix| :func:`datasets.fetch_california_housing`,
:func:`datasets.fetch_covtype`,
:func:`datasets.fetch_kddcup99`, :func:`datasets.fetch_olivetti_faces`,
:func:`datasets.fetch_rcv1`, and :func:`datasets.fetch_species_distributions`
try to persist the previously cache using the new ``joblib`` if the cached
data was persisted using the deprecated ``sklearn.externals.joblib``. This
behavior is set to be deprecated and removed in v0.23.
:pr:`14197` by `Adrin Jalali`_.
:mod:`sklearn.ensemble`
.......................
- |Fix| Fix zero division error in :func:`HistGradientBoostingClassifier` and
:func:`HistGradientBoostingRegressor`.
:pr:`14024` by `Nicolas Hug `.
:mod:`sklearn.impute`
.....................
- |Fix| Fixed a bug in :class:`impute.SimpleImputer` and
:class:`impute.IterativeImputer` so that no errors are thrown when there are
missing values in training data. :pr:`13974` by `Frank Hoang `.
:mod:`sklearn.inspection`
.........................
- |Fix| Fixed a bug in :func:`inspection.plot_partial_dependence` where
``target`` parameter was not being taken into account for multiclass problems.
:pr:`14393` by :user:`Guillem G. Subies `.
:mod:`sklearn.linear_model`
...........................
- |Fix| Fixed a bug in :class:`linear_model.LogisticRegressionCV` where
``refit=False`` would fail depending on the ``'multiclass'`` and
``'penalty'`` parameters (regression introduced in 0.21). :pr:`14087` by
`Nicolas Hug`_.
- |Fix| Compatibility fix for :class:`linear_model.ARDRegression` and
Scipy>=1.3.0. Adapts to upstream changes to the default `pinvh` cutoff
threshold which otherwise results in poor accuracy in some cases.
:pr:`14067` by :user:`Tim Staley `.
:mod:`sklearn.neighbors`
........................
- |Fix| Fixed a bug in :class:`neighbors.NeighborhoodComponentsAnalysis` where
the validation of initial parameters ``n_components``, ``max_iter`` and
``tol`` required too strict types. :pr:`14092` by
:user:`Jérémie du Boisberranger `.
:mod:`sklearn.tree`
...................
- |Fix| Fixed bug in :func:`tree.export_text` when the tree has one feature and
a single feature name is passed in. :pr:`14053` by `Thomas Fan`.
- |Fix| Fixed an issue with :func:`plot_tree` where it displayed
entropy calculations even for `gini` criterion in DecisionTreeClassifiers.
:pr:`13947` by :user:`Frank Hoang `.
.. _changes_0_21_2:
Version 0.21.2
==============
**24 May 2019**
Changelog
---------
:mod:`sklearn.decomposition`
............................
- |Fix| Fixed a bug in :class:`cross_decomposition.CCA` improving numerical
stability when `Y` is close to zero. :pr:`13903` by `Thomas Fan`_.
:mod:`sklearn.metrics`
......................
- |Fix| Fixed a bug in :func:`metrics.pairwise.euclidean_distances` where a
part of the distance matrix was left un-instanciated for suffiently large
float32 datasets (regression introduced in 0.21). :pr:`13910` by
:user:`Jérémie du Boisberranger `.
:mod:`sklearn.preprocessing`
............................
- |Fix| Fixed a bug in :class:`preprocessing.OneHotEncoder` where the new
`drop` parameter was not reflected in `get_feature_names`. :pr:`13894`
by :user:`James Myatt `.
:mod:`sklearn.utils.sparsefuncs`
................................
- |Fix| Fixed a bug where :func:`min_max_axis` would fail on 32-bit systems
for certain large inputs. This affects :class:`preprocessing.MaxAbsScaler`,
:func:`preprocessing.normalize` and :class:`preprocessing.LabelBinarizer`.
:pr:`13741` by :user:`Roddy MacSween `.
.. _changes_0_21_1:
Version 0.21.1
==============
**17 May 2019**
This is a bug-fix release to primarily resolve some packaging issues in version
0.21.0. It also includes minor documentation improvements and some bug fixes.
Changelog
---------
:mod:`sklearn.inspection`
.........................
- |Fix| Fixed a bug in :func:`inspection.partial_dependence` to only check
classifier and not regressor for the multiclass-multioutput case.
:pr:`14309` by :user:`Guillaume Lemaitre `.
:mod:`sklearn.metrics`
......................
- |Fix| Fixed a bug in :class:`metrics.pairwise_distances` where it would raise
``AttributeError`` for boolean metrics when ``X`` had a boolean dtype and
``Y == None``.
:issue:`13864` by :user:`Paresh Mathur `.
- |Fix| Fixed two bugs in :class:`metrics.pairwise_distances` when
``n_jobs > 1``. First it used to return a distance matrix with same dtype as
input, even for integer dtype. Then the diagonal was not zeros for euclidean
metric when ``Y`` is ``X``. :issue:`13877` by
:user:`Jérémie du Boisberranger `.
:mod:`sklearn.neighbors`
........................
- |Fix| Fixed a bug in :class:`neighbors.KernelDensity` which could not be
restored from a pickle if ``sample_weight`` had been used.
:issue:`13772` by :user:`Aditya Vyas `.
.. _changes_0_21:
Version 0.21.0
==============
**May 2019**
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.
- :class:`discriminant_analysis.LinearDiscriminantAnalysis` for multiclass
classification. |Fix|
- :class:`discriminant_analysis.LinearDiscriminantAnalysis` with 'eigen'
solver. |Fix|
- :class:`linear_model.BayesianRidge` |Fix|
- Decision trees and derived ensembles when both `max_depth` and
`max_leaf_nodes` are set. |Fix|
- :class:`linear_model.LogisticRegression` and
:class:`linear_model.LogisticRegressionCV` with 'saga' solver. |Fix|
- :class:`ensemble.GradientBoostingClassifier` |Fix|
- :class:`sklearn.feature_extraction.text.HashingVectorizer`,
:class:`sklearn.feature_extraction.text.TfidfVectorizer`, and
:class:`sklearn.feature_extraction.text.CountVectorizer` |Fix|
- :class:`neural_network.MLPClassifier` |Fix|
- :func:`svm.SVC.decision_function` and
:func:`multiclass.OneVsOneClassifier.decision_function`. |Fix|
- :class:`linear_model.SGDClassifier` and any derived classifiers. |Fix|
- Any model using the :func:`linear_model._sag.sag_solver` function with a `0`
seed, including :class:`linear_model.LogisticRegression`,
:class:`linear_model.LogisticRegressionCV`, :class:`linear_model.Ridge`,
and :class:`linear_model.RidgeCV` with 'sag' solver. |Fix|
- :class:`linear_model.RidgeCV` when using leave-one-out cross-validation
with sparse inputs. |Fix|
Details are listed in the changelog below.
(While we are trying to better inform users by providing this information, we
cannot assure that this list is complete.)
Known Major Bugs
----------------
* The default `max_iter` for :class:`linear_model.LogisticRegression` is too
small for many solvers given the default `tol`. In particular, we
accidentally changed the default `max_iter` for the liblinear solver from
1000 to 100 iterations in :pr:`3591` released in version 0.16.
In a future release we hope to choose better default `max_iter` and `tol`
heuristically depending on the solver (see :pr:`13317`).
Changelog
---------
Support for Python 3.4 and below has been officially dropped.
..
Entries should be grouped by module (in alphabetic order) and prefixed with
one of the labels: |MajorFeature|, |Feature|, |Efficiency|, |Enhancement|,
|Fix| or |API| (see whats_new.rst for descriptions).
Entries should be ordered by those labels (e.g. |Fix| after |Efficiency|).
Changes not specific to a module should be listed under *Multiple Modules*
or *Miscellaneous*.
Entries should end with:
:pr:`123456` by :user:`Joe Bloggs `.
where 123456 is the *pull request* number, not the issue number.
:mod:`sklearn.base`
...................
- |API| The R2 score used when calling ``score`` on a regressor will use
``multioutput='uniform_average'`` from version 0.23 to keep consistent with
:func:`metrics.r2_score`. This will influence the ``score`` method of all
the multioutput regressors (except for
:class:`multioutput.MultiOutputRegressor`).
:pr:`13157` by :user:`Hanmin Qin `.
:mod:`sklearn.calibration`
..........................
- |Enhancement| Added support to bin the data passed into
:class:`calibration.calibration_curve` by quantiles instead of uniformly
between 0 and 1.
:pr:`13086` by :user:`Scott Cole `.
- |Enhancement| Allow n-dimensional arrays as input for
`calibration.CalibratedClassifierCV`. :pr:`13485` by
:user:`William de Vazelhes `.
:mod:`sklearn.cluster`
......................
- |MajorFeature| A new clustering algorithm: :class:`cluster.OPTICS`: an
algorithm related to :class:`cluster.DBSCAN`, that has hyperparameters easier
to set and that scales better, by :user:`Shane `,
`Adrin Jalali`_, :user:`Erich Schubert `, `Hanmin Qin`_, and
:user:`Assia Benbihi `.
- |Fix| Fixed a bug where :class:`cluster.Birch` could occasionally raise an
AttributeError. :pr:`13651` by `Joel Nothman`_.
- |Fix| Fixed a bug in :class:`cluster.KMeans` where empty clusters weren't
correctly relocated when using sample weights. :pr:`13486` by
:user:`Jérémie du Boisberranger `.
- |API| The ``n_components_`` attribute in :class:`cluster.AgglomerativeClustering`
and :class:`cluster.FeatureAgglomeration` has been renamed to
``n_connected_components_``.
:pr:`13427` by :user:`Stephane Couvreur `.
- |Enhancement| :class:`cluster.AgglomerativeClustering` and
:class:`cluster.FeatureAgglomeration` now accept a ``distance_threshold``
parameter which can be used to find the clusters instead of ``n_clusters``.
:issue:`9069` by :user:`Vathsala Achar ` and `Adrin Jalali`_.
:mod:`sklearn.compose`
......................
- |API| :class:`compose.ColumnTransformer` is no longer an experimental
feature. :pr:`13835` by :user:`Hanmin Qin `.
:mod:`sklearn.datasets`
.......................
- |Fix| Added support for 64-bit group IDs and pointers in SVMLight files.
:pr:`10727` by :user:`Bryan K Woods `.
- |Fix| :func:`datasets.load_sample_images` returns images with a deterministic
order. :pr:`13250` by :user:`Thomas Fan `.
:mod:`sklearn.decomposition`
............................
- |Enhancement| :class:`decomposition.KernelPCA` now has deterministic output
(resolved sign ambiguity in eigenvalue decomposition of the kernel matrix).
:pr:`13241` by :user:`Aurélien Bellet `.
- |Fix| Fixed a bug in :class:`decomposition.KernelPCA`, `fit().transform()`
now produces the correct output (the same as `fit_transform()`) in case
of non-removed zero eigenvalues (`remove_zero_eig=False`).
`fit_inverse_transform` was also accelerated by using the same trick as
`fit_transform` to compute the transform of `X`.
:pr:`12143` by :user:`Sylvain Marié `
- |Fix| Fixed a bug in :class:`decomposition.NMF` where `init = 'nndsvd'`,
`init = 'nndsvda'`, and `init = 'nndsvdar'` are allowed when
`n_components < n_features` instead of
`n_components <= min(n_samples, n_features)`.
:pr:`11650` by :user:`Hossein Pourbozorg ` and
:user:`Zijie (ZJ) Poh `.
- |API| The default value of the :code:`init` argument in
:func:`decomposition.non_negative_factorization` will change from
:code:`random` to :code:`None` in version 0.23 to make it consistent with
:class:`decomposition.NMF`. A FutureWarning is raised when
the default value is used.
:pr:`12988` by :user:`Zijie (ZJ) Poh `.
:mod:`sklearn.discriminant_analysis`
....................................
- |Enhancement| :class:`discriminant_analysis.LinearDiscriminantAnalysis` now
preserves ``float32`` and ``float64`` dtypes. :pr:`8769` and
:pr:`11000` by :user:`Thibault Sejourne `
- |Fix| A ``ChangedBehaviourWarning`` is now raised when
:class:`discriminant_analysis.LinearDiscriminantAnalysis` is given as
parameter ``n_components > min(n_features, n_classes - 1)``, and
``n_components`` is changed to ``min(n_features, n_classes - 1)`` if so.
Previously the change was made, but silently. :pr:`11526` by
:user:`William de Vazelhes`.
- |Fix| Fixed a bug in :class:`discriminant_analysis.LinearDiscriminantAnalysis`
where the predicted probabilities would be incorrectly computed in the
multiclass case. :pr:`6848`, by :user:`Agamemnon Krasoulis
` and `Guillaume Lemaitre `.
- |Fix| Fixed a bug in :class:`discriminant_analysis.LinearDiscriminantAnalysis`
where the predicted probabilities would be incorrectly computed with ``eigen``
solver. :pr:`11727`, by :user:`Agamemnon Krasoulis
`.
:mod:`sklearn.dummy`
....................
- |Fix| Fixed a bug in :class:`dummy.DummyClassifier` where the
``predict_proba`` method was returning int32 array instead of
float64 for the ``stratified`` strategy. :pr:`13266` by
:user:`Christos Aridas`.
- |Fix| Fixed a bug in :class:`dummy.DummyClassifier` where it was throwing a
dimension mismatch error in prediction time if a column vector ``y`` with
``shape=(n, 1)`` was given at ``fit`` time. :pr:`13545` by :user:`Nick
Sorros ` and `Adrin Jalali`_.
:mod:`sklearn.ensemble`
.......................
- |MajorFeature| Add two new implementations of
gradient boosting trees: :class:`ensemble.HistGradientBoostingClassifier`
and :class:`ensemble.HistGradientBoostingRegressor`. The implementation of
these estimators is inspired by
`LightGBM `_ and can be orders of
magnitude faster than :class:`ensemble.GradientBoostingRegressor` and
:class:`ensemble.GradientBoostingClassifier` when the number of samples is
larger than tens of thousands of samples. The API of these new estimators
is slightly different, and some of the features from
:class:`ensemble.GradientBoostingClassifier` and
:class:`ensemble.GradientBoostingRegressor` are not yet supported.
These new estimators are experimental, which means that their results or
their API might change without any deprecation cycle. To use them, you
need to explicitly import ``enable_hist_gradient_boosting``::
>>> # explicitly require this experimental feature
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
>>> # now you can import normally from sklearn.ensemble
>>> from sklearn.ensemble import HistGradientBoostingClassifier
.. note::
Update: since version 1.0, these estimators are not experimental
anymore and you don't need to use `from sklearn.experimental import
enable_hist_gradient_boosting`.
:pr:`12807` by :user:`Nicolas Hug`.
- |Feature| Add :class:`ensemble.VotingRegressor`
which provides an equivalent of :class:`ensemble.VotingClassifier`
for regression problems.
:pr:`12513` by :user:`Ramil Nugmanov ` and
:user:`Mohamed Ali Jamaoui `.
- |Efficiency| Make :class:`ensemble.IsolationForest` prefer threads over
processes when running with ``n_jobs > 1`` as the underlying decision tree
fit calls do release the GIL. This changes reduces memory usage and
communication overhead. :pr:`12543` by :user:`Isaac Storch `
and `Olivier Grisel`_.
- |Efficiency| Make :class:`ensemble.IsolationForest` more memory efficient
by avoiding keeping in memory each tree prediction. :pr:`13260` by
`Nicolas Goix`_.
- |Efficiency| :class:`ensemble.IsolationForest` now uses chunks of data at
prediction step, thus capping the memory usage. :pr:`13283` by
`Nicolas Goix`_.
- |Efficiency| :class:`sklearn.ensemble.GradientBoostingClassifier` and
:class:`sklearn.ensemble.GradientBoostingRegressor` now keep the
input ``y`` as ``float64`` to avoid it being copied internally by trees.
:pr:`13524` by `Adrin Jalali`_.
- |Enhancement| Minimized the validation of X in
:class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor`
:pr:`13174` by :user:`Christos Aridas `.
- |Enhancement| :class:`ensemble.IsolationForest` now exposes ``warm_start``
parameter, allowing iterative addition of trees to an isolation
forest. :pr:`13496` by :user:`Peter Marko `.
- |Fix| The values of ``feature_importances_`` in all random forest based
models (i.e.
:class:`ensemble.RandomForestClassifier`,
:class:`ensemble.RandomForestRegressor`,
:class:`ensemble.ExtraTreesClassifier`,
:class:`ensemble.ExtraTreesRegressor`,
:class:`ensemble.RandomTreesEmbedding`,
:class:`ensemble.GradientBoostingClassifier`, and
:class:`ensemble.GradientBoostingRegressor`) now:
- sum up to ``1``
- all the single node trees in feature importance calculation are ignored
- in case all trees have only one single node (i.e. a root node),
feature importances will be an array of all zeros.
:pr:`13636` and :pr:`13620` by `Adrin Jalali`_.
- |Fix| Fixed a bug in :class:`ensemble.GradientBoostingClassifier` and
:class:`ensemble.GradientBoostingRegressor`, which didn't support
scikit-learn estimators as the initial estimator. Also added support of
initial estimator which does not support sample weights. :pr:`12436` by
:user:`Jérémie du Boisberranger ` and :pr:`12983` by
:user:`Nicolas Hug`.
- |Fix| Fixed the output of the average path length computed in
:class:`ensemble.IsolationForest` when the input is either 0, 1 or 2.
:pr:`13251` by :user:`Albert Thomas `
and :user:`joshuakennethjones `.
- |Fix| Fixed a bug in :class:`ensemble.GradientBoostingClassifier` where
the gradients would be incorrectly computed in multiclass classification
problems. :pr:`12715` by :user:`Nicolas Hug`.
- |Fix| Fixed a bug in :class:`ensemble.GradientBoostingClassifier` where
validation sets for early stopping were not sampled with stratification.
:pr:`13164` by :user:`Nicolas Hug`.
- |Fix| Fixed a bug in :class:`ensemble.GradientBoostingClassifier` where
the default initial prediction of a multiclass classifier would predict the
classes priors instead of the log of the priors. :pr:`12983` by
:user:`Nicolas Hug`.
- |Fix| Fixed a bug in :class:`ensemble.RandomForestClassifier` where the
``predict`` method would error for multiclass multioutput forests models
if any targets were strings. :pr:`12834` by :user:`Elizabeth Sander
`.
- |Fix| Fixed a bug in :class:`ensemble.gradient_boosting.LossFunction` and
:class:`ensemble.gradient_boosting.LeastSquaresError` where the default
value of ``learning_rate`` in ``update_terminal_regions`` is not consistent
with the document and the caller functions. Note however that directly using
these loss functions is deprecated.
:pr:`6463` by :user:`movelikeriver `.
- |Fix| :func:`ensemble.partial_dependence` (and consequently the new
version :func:`sklearn.inspection.partial_dependence`) now takes sample
weights into account for the partial dependence computation when the
gradient boosting model has been trained with sample weights.
:pr:`13193` by :user:`Samuel O. Ronsin `.
- |API| :func:`ensemble.partial_dependence` and
:func:`ensemble.plot_partial_dependence` are now deprecated in favor of
:func:`inspection.partial_dependence`
and
:func:`inspection.plot_partial_dependence`.
:pr:`12599` by :user:`Trevor Stephens` and
:user:`Nicolas Hug`.
- |Fix| :class:`ensemble.VotingClassifier` and
:class:`ensemble.VotingRegressor` were failing during ``fit`` in one
of the estimators was set to ``None`` and ``sample_weight`` was not ``None``.
:pr:`13779` by :user:`Guillaume Lemaitre `.
- |API| :class:`ensemble.VotingClassifier` and
:class:`ensemble.VotingRegressor` accept ``'drop'`` to disable an estimator
in addition to ``None`` to be consistent with other estimators (i.e.,
:class:`pipeline.FeatureUnion` and :class:`compose.ColumnTransformer`).
:pr:`13780` by :user:`Guillaume Lemaitre `.
:mod:`sklearn.externals`
........................
- |API| Deprecated :mod:`externals.six` since we have dropped support for
Python 2.7. :pr:`12916` by :user:`Hanmin Qin `.
:mod:`sklearn.feature_extraction`
.................................
- |Fix| If ``input='file'`` or ``input='filename'``, and a callable is given as
the ``analyzer``, :class:`sklearn.feature_extraction.text.HashingVectorizer`,
:class:`sklearn.feature_extraction.text.TfidfVectorizer`, and
:class:`sklearn.feature_extraction.text.CountVectorizer` now read the data
from the file(s) and then pass it to the given ``analyzer``, instead of
passing the file name(s) or the file object(s) to the analyzer.
:pr:`13641` by `Adrin Jalali`_.
:mod:`sklearn.impute`
.....................
- |MajorFeature| Added :class:`impute.IterativeImputer`, which is a strategy
for imputing missing values by modeling each feature with missing values as a
function of other features in a round-robin fashion. :pr:`8478` and
:pr:`12177` by :user:`Sergey Feldman ` and :user:`Ben Lawson
`.
The API of IterativeImputer is experimental and subject to change without any
deprecation cycle. To use them, you need to explicitly import
``enable_iterative_imputer``::
>>> from sklearn.experimental import enable_iterative_imputer # noqa
>>> # now you can import normally from sklearn.impute
>>> from sklearn.impute import IterativeImputer
- |Feature| The :class:`impute.SimpleImputer` and
:class:`impute.IterativeImputer` have a new parameter ``'add_indicator'``,
which simply stacks a :class:`impute.MissingIndicator` transform into the
output of the imputer's transform. That allows a predictive estimator to
account for missingness. :pr:`12583`, :pr:`13601` by :user:`Danylo Baibak
`.
- |Fix| In :class:`impute.MissingIndicator` avoid implicit densification by
raising an exception if input is sparse add `missing_values` property
is set to 0. :pr:`13240` by :user:`Bartosz Telenczuk `.
- |Fix| Fixed two bugs in :class:`impute.MissingIndicator`. First, when
``X`` is sparse, all the non-zero non missing values used to become
explicit False in the transformed data. Then, when
``features='missing-only'``, all features used to be kept if there were no
missing values at all. :pr:`13562` by :user:`Jérémie du Boisberranger
`.
:mod:`sklearn.inspection`
.........................
(new subpackage)
- |Feature| Partial dependence plots
(:func:`inspection.plot_partial_dependence`) are now supported for
any regressor or classifier (provided that they have a `predict_proba`
method). :pr:`12599` by :user:`Trevor Stephens ` and
:user:`Nicolas Hug `.
:mod:`sklearn.isotonic`
.......................
- |Feature| Allow different dtypes (such as float32) in
:class:`isotonic.IsotonicRegression`.
:pr:`8769` by :user:`Vlad Niculae `
:mod:`sklearn.linear_model`
...........................
- |Enhancement| :class:`linear_model.Ridge` now preserves ``float32`` and
``float64`` dtypes. :issue:`8769` and :issue:`11000` by
:user:`Guillaume Lemaitre `, and :user:`Joan Massich `
- |Feature| :class:`linear_model.LogisticRegression` and
:class:`linear_model.LogisticRegressionCV` now support Elastic-Net penalty,
with the 'saga' solver. :pr:`11646` by :user:`Nicolas Hug `.
- |Feature| Added :class:`linear_model.lars_path_gram`, which is
:class:`linear_model.lars_path` in the sufficient stats mode, allowing
users to compute :class:`linear_model.lars_path` without providing
``X`` and ``y``. :pr:`11699` by :user:`Kuai Yu `.
- |Efficiency| :func:`linear_model.make_dataset` now preserves
``float32`` and ``float64`` dtypes, reducing memory consumption in stochastic
gradient, SAG and SAGA solvers.
:pr:`8769` and :pr:`11000` by
:user:`Nelle Varoquaux `, :user:`Arthur Imbert `,
:user:`Guillaume Lemaitre `, and :user:`Joan Massich `
- |Enhancement| :class:`linear_model.LogisticRegression` now supports an
unregularized objective when ``penalty='none'`` is passed. This is
equivalent to setting ``C=np.inf`` with l2 regularization. Not supported
by the liblinear solver. :pr:`12860` by :user:`Nicolas Hug
`.
- |Enhancement| `sparse_cg` solver in :class:`linear_model.Ridge`
now supports fitting the intercept (i.e. ``fit_intercept=True``) when
inputs are sparse. :pr:`13336` by :user:`Bartosz Telenczuk `.
- |Enhancement| The coordinate descent solver used in `Lasso`, `ElasticNet`,
etc. now issues a `ConvergenceWarning` when it completes without meeting the
desired toleranbce.
:pr:`11754` and :pr:`13397` by :user:`Brent Fagan ` and
:user:`Adrin Jalali `.
- |Fix| Fixed a bug in :class:`linear_model.LogisticRegression` and
:class:`linear_model.LogisticRegressionCV` with 'saga' solver, where the
weights would not be correctly updated in some cases.
:pr:`11646` by `Tom Dupre la Tour`_.
- |Fix| Fixed the posterior mean, posterior covariance and returned
regularization parameters in :class:`linear_model.BayesianRidge`. The
posterior mean and the posterior covariance were not the ones computed
with the last update of the regularization parameters and the returned
regularization parameters were not the final ones. Also fixed the formula of
the log marginal likelihood used to compute the score when
`compute_score=True`. :pr:`12174` by
:user:`Albert Thomas `.
- |Fix| Fixed a bug in :class:`linear_model.LassoLarsIC`, where user input
``copy_X=False`` at instance creation would be overridden by default
parameter value ``copy_X=True`` in ``fit``.
:pr:`12972` by :user:`Lucio Fernandez-Arjona `
- |Fix| Fixed a bug in :class:`linear_model.LinearRegression` that
was not returning the same coeffecients and intercepts with
``fit_intercept=True`` in sparse and dense case.
:pr:`13279` by `Alexandre Gramfort`_
- |Fix| Fixed a bug in :class:`linear_model.HuberRegressor` that was
broken when ``X`` was of dtype bool. :pr:`13328` by `Alexandre Gramfort`_.
- |Fix| Fixed a performance issue of ``saga`` and ``sag`` solvers when called
in a :class:`joblib.Parallel` setting with ``n_jobs > 1`` and
``backend="threading"``, causing them to perform worse than in the sequential
case. :pr:`13389` by :user:`Pierre Glaser `.
- |Fix| Fixed a bug in
:class:`linear_model.stochastic_gradient.BaseSGDClassifier` that was not
deterministic when trained in a multi-class setting on several threads.
:pr:`13422` by :user:`Clément Doumouro `.
- |Fix| Fixed bug in :func:`linear_model.ridge_regression`,
:class:`linear_model.Ridge` and
:class:`linear_model.RidgeClassifier` that
caused unhandled exception for arguments ``return_intercept=True`` and
``solver=auto`` (default) or any other solver different from ``sag``.
:pr:`13363` by :user:`Bartosz Telenczuk `
- |Fix| :func:`linear_model.ridge_regression` will now raise an exception
if ``return_intercept=True`` and solver is different from ``sag``. Previously,
only warning was issued. :pr:`13363` by :user:`Bartosz Telenczuk `
- |Fix| :func:`linear_model.ridge_regression` will choose ``sparse_cg``
solver for sparse inputs when ``solver=auto`` and ``sample_weight``
is provided (previously `cholesky` solver was selected).
:pr:`13363` by :user:`Bartosz Telenczuk `
- |API| The use of :class:`linear_model.lars_path` with ``X=None``
while passing ``Gram`` is deprecated in version 0.21 and will be removed
in version 0.23. Use :class:`linear_model.lars_path_gram` instead.
:pr:`11699` by :user:`Kuai Yu `.
- |API| :func:`linear_model.logistic_regression_path` is deprecated
in version 0.21 and will be removed in version 0.23.
:pr:`12821` by :user:`Nicolas Hug `.
- |Fix| :class:`linear_model.RidgeCV` with leave-one-out cross-validation
now correctly fits an intercept when ``fit_intercept=True`` and the design
matrix is sparse. :issue:`13350` by :user:`Jérôme Dockès `
:mod:`sklearn.manifold`
.......................
- |Efficiency| Make :func:`manifold.tsne.trustworthiness` use an inverted index
instead of an `np.where` lookup to find the rank of neighbors in the input
space. This improves efficiency in particular when computed with
lots of neighbors and/or small datasets.
:pr:`9907` by :user:`William de Vazelhes `.
:mod:`sklearn.metrics`
......................
- |Feature| Added the :func:`metrics.max_error` metric and a corresponding
``'max_error'`` scorer for single output regression.
:pr:`12232` by :user:`Krishna Sangeeth `.
- |Feature| Add :func:`metrics.multilabel_confusion_matrix`, which calculates a
confusion matrix with true positive, false positive, false negative and true
negative counts for each class. This facilitates the calculation of set-wise
metrics such as recall, specificity, fall out and miss rate.
:pr:`11179` by :user:`Shangwu Yao ` and `Joel Nothman`_.
- |Feature| :func:`metrics.jaccard_score` has been added to calculate the
Jaccard coefficient as an evaluation metric for binary, multilabel and
multiclass tasks, with an interface analogous to :func:`metrics.f1_score`.
:pr:`13151` by :user:`Gaurav Dhingra ` and `Joel Nothman`_.
- |Feature| Added :func:`metrics.pairwise.haversine_distances` which can be
accessed with `metric='pairwise'` through :func:`metrics.pairwise_distances`
and estimators. (Haversine distance was previously available for nearest
neighbors calculation.) :pr:`12568` by :user:`Wei Xue `,
:user:`Emmanuel Arias ` and `Joel Nothman`_.
- |Efficiency| Faster :func:`metrics.pairwise_distances` with `n_jobs`
> 1 by using a thread-based backend, instead of process-based backends.
:pr:`8216` by :user:`Pierre Glaser ` and
:user:`Romuald Menuet `
- |Efficiency| The pairwise manhattan distances with sparse input now uses the
BLAS shipped with scipy instead of the bundled BLAS. :pr:`12732` by
:user:`Jérémie du Boisberranger `
- |Enhancement| Use label `accuracy` instead of `micro-average` on
:func:`metrics.classification_report` to avoid confusion. `micro-average` is
only shown for multi-label or multi-class with a subset of classes because
it is otherwise identical to accuracy.
:pr:`12334` by :user:`Emmanuel Arias `,
`Joel Nothman`_ and `Andreas Müller`_
- |Enhancement| Added `beta` parameter to
:func:`metrics.homogeneity_completeness_v_measure` and
:func:`metrics.v_measure_score` to configure the
tradeoff between homogeneity and completeness.
:pr:`13607` by :user:`Stephane Couvreur ` and
and :user:`Ivan Sanchez `.
- |Fix| The metric :func:`metrics.r2_score` is degenerate with a single sample
and now it returns NaN and raises :class:`exceptions.UndefinedMetricWarning`.
:pr:`12855` by :user:`Pawel Sendyk `.
- |Fix| Fixed a bug where :func:`metrics.brier_score_loss` will sometimes
return incorrect result when there's only one class in ``y_true``.
:pr:`13628` by :user:`Hanmin Qin `.
- |Fix| Fixed a bug in :func:`metrics.label_ranking_average_precision_score`
where sample_weight wasn't taken into account for samples with degenerate
labels.
:pr:`13447` by :user:`Dan Ellis `.
- |API| The parameter ``labels`` in :func:`metrics.hamming_loss` is deprecated
in version 0.21 and will be removed in version 0.23. :pr:`10580` by
:user:`Reshama Shaikh ` and :user:`Sandra Mitrovic `.
- |Fix| The function :func:`metrics.pairwise.euclidean_distances`, and
therefore several estimators with ``metric='euclidean'``, suffered from
numerical precision issues with ``float32`` features. Precision has been
increased at the cost of a small drop of performance. :pr:`13554` by
:user:`Celelibi` and :user:`Jérémie du Boisberranger `.
- |API| :func:`metrics.jaccard_similarity_score` is deprecated in favour of
the more consistent :func:`metrics.jaccard_score`. The former behavior for
binary and multiclass targets is broken.
:pr:`13151` by `Joel Nothman`_.
:mod:`sklearn.mixture`
......................
- |Fix| Fixed a bug in :class:`mixture.BaseMixture` and therefore on estimators
based on it, i.e. :class:`mixture.GaussianMixture` and
:class:`mixture.BayesianGaussianMixture`, where ``fit_predict`` and
``fit.predict`` were not equivalent. :pr:`13142` by
:user:`Jérémie du Boisberranger `.
:mod:`sklearn.model_selection`
..............................
- |Feature| Classes :class:`~model_selection.GridSearchCV` and
:class:`~model_selection.RandomizedSearchCV` now allow for refit=callable
to add flexibility in identifying the best estimator.
See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py`.
:pr:`11354` by :user:`Wenhao Zhang `,
`Joel Nothman`_ and :user:`Adrin Jalali `.
- |Enhancement| Classes :class:`~model_selection.GridSearchCV`,
:class:`~model_selection.RandomizedSearchCV`, and methods
:func:`~model_selection.cross_val_score`,
:func:`~model_selection.cross_val_predict`,
:func:`~model_selection.cross_validate`, now print train scores when
`return_train_scores` is True and `verbose` > 2. For
:func:`~model_selection.learning_curve`, and
:func:`~model_selection.validation_curve` only the latter is required.
:pr:`12613` and :pr:`12669` by :user:`Marc Torrellas `.
- |Enhancement| Some :term:`CV splitter` classes and
`model_selection.train_test_split` now raise ``ValueError`` when the
resulting training set is empty.
:pr:`12861` by :user:`Nicolas Hug `.
- |Fix| Fixed a bug where :class:`model_selection.StratifiedKFold`
shuffles each class's samples with the same ``random_state``,
making ``shuffle=True`` ineffective.
:pr:`13124` by :user:`Hanmin Qin `.
- |Fix| Added ability for :func:`model_selection.cross_val_predict` to handle
multi-label (and multioutput-multiclass) targets with ``predict_proba``-type
methods. :pr:`8773` by :user:`Stephen Hoover `.
- |Fix| Fixed an issue in :func:`~model_selection.cross_val_predict` where
`method="predict_proba"` returned always `0.0` when one of the classes was
excluded in a cross-validation fold.
:pr:`13366` by :user:`Guillaume Fournier `
:mod:`sklearn.multiclass`
.........................
- |Fix| Fixed an issue in :func:`multiclass.OneVsOneClassifier.decision_function`
where the decision_function value of a given sample was different depending on
whether the decision_function was evaluated on the sample alone or on a batch
containing this same sample due to the scaling used in decision_function.
:pr:`10440` by :user:`Jonathan Ohayon `.
:mod:`sklearn.multioutput`
..........................
- |Fix| Fixed a bug in :class:`multioutput.MultiOutputClassifier` where the
`predict_proba` method incorrectly checked for `predict_proba` attribute in
the estimator object.
:pr:`12222` by :user:`Rebekah Kim `
:mod:`sklearn.neighbors`
........................
- |MajorFeature| Added :class:`neighbors.NeighborhoodComponentsAnalysis` for
metric learning, which implements the Neighborhood Components Analysis
algorithm. :pr:`10058` by :user:`William de Vazelhes ` and
:user:`John Chiotellis `.
- |API| Methods in :class:`neighbors.NearestNeighbors` :
:func:`~neighbors.NearestNeighbors.kneighbors`,
:func:`~neighbors.NearestNeighbors.radius_neighbors`,
:func:`~neighbors.NearestNeighbors.kneighbors_graph`,
:func:`~neighbors.NearestNeighbors.radius_neighbors_graph`
now raise ``NotFittedError``, rather than ``AttributeError``,
when called before ``fit`` :pr:`12279` by :user:`Krishna Sangeeth
`.
:mod:`sklearn.neural_network`
.............................
- |Fix| Fixed a bug in :class:`neural_network.MLPClassifier` and
:class:`neural_network.MLPRegressor` where the option :code:`shuffle=False`
was being ignored. :pr:`12582` by :user:`Sam Waterbury `.
- |Fix| Fixed a bug in :class:`neural_network.MLPClassifier` where
validation sets for early stopping were not sampled with stratification. In
the multilabel case however, splits are still not stratified.
:pr:`13164` by :user:`Nicolas Hug`.
:mod:`sklearn.pipeline`
.......................
- |Feature| :class:`pipeline.Pipeline` can now use indexing notation (e.g.
``my_pipeline[0:-1]``) to extract a subsequence of steps as another Pipeline
instance. A Pipeline can also be indexed directly to extract a particular
step (e.g. ``my_pipeline['svc']``), rather than accessing ``named_steps``.
:pr:`2568` by `Joel Nothman`_.
- |Feature| Added optional parameter ``verbose`` in :class:`pipeline.Pipeline`,
:class:`compose.ColumnTransformer` and :class:`pipeline.FeatureUnion`
and corresponding ``make_`` helpers for showing progress and timing of
each step. :pr:`11364` by :user:`Baze Petrushev `,
:user:`Karan Desai `, `Joel Nothman`_, and
:user:`Thomas Fan `.
- |Enhancement| :class:`pipeline.Pipeline` now supports using ``'passthrough'``
as a transformer, with the same effect as ``None``.
:pr:`11144` by :user:`Thomas Fan `.
- |Enhancement| :class:`pipeline.Pipeline` implements ``__len__`` and
therefore ``len(pipeline)`` returns the number of steps in the pipeline.
:pr:`13439` by :user:`Lakshya KD `.
:mod:`sklearn.preprocessing`
............................
- |Feature| :class:`preprocessing.OneHotEncoder` now supports dropping one
feature per category with a new drop parameter. :pr:`12908` by
:user:`Drew Johnston `.
- |Efficiency| :class:`preprocessing.OneHotEncoder` and
:class:`preprocessing.OrdinalEncoder` now handle pandas DataFrames more
efficiently. :pr:`13253` by :user:`maikia`.
- |Efficiency| Make :class:`preprocessing.MultiLabelBinarizer` cache class
mappings instead of calculating it every time on the fly.
:pr:`12116` by :user:`Ekaterina Krivich ` and `Joel Nothman`_.
- |Efficiency| :class:`preprocessing.PolynomialFeatures` now supports
compressed sparse row (CSR) matrices as input for degrees 2 and 3. This is
typically much faster than the dense case as it scales with matrix density
and expansion degree (on the order of density^degree), and is much, much
faster than the compressed sparse column (CSC) case.
:pr:`12197` by :user:`Andrew Nystrom `.
- |Efficiency| Speed improvement in :class:`preprocessing.PolynomialFeatures`,
in the dense case. Also added a new parameter ``order`` which controls output
order for further speed performances. :pr:`12251` by `Tom Dupre la Tour`_.
- |Fix| Fixed the calculation overflow when using a float16 dtype with
:class:`preprocessing.StandardScaler`.
:pr:`13007` by :user:`Raffaello Baluyot `
- |Fix| Fixed a bug in :class:`preprocessing.QuantileTransformer` and
:func:`preprocessing.quantile_transform` to force n_quantiles to be at most
equal to n_samples. Values of n_quantiles larger than n_samples were either
useless or resulting in a wrong approximation of the cumulative distribution
function estimator. :pr:`13333` by :user:`Albert Thomas `.
- |API| The default value of `copy` in :func:`preprocessing.quantile_transform`
will change from False to True in 0.23 in order to make it more consistent
with the default `copy` values of other functions in
:mod:`preprocessing` and prevent unexpected side effects by modifying
the value of `X` inplace.
:pr:`13459` by :user:`Hunter McGushion `.
:mod:`sklearn.svm`
..................
- |Fix| Fixed an issue in :func:`svm.SVC.decision_function` when
``decision_function_shape='ovr'``. The decision_function value of a given
sample was different depending on whether the decision_function was evaluated
on the sample alone or on a batch containing this same sample due to the
scaling used in decision_function.
:pr:`10440` by :user:`Jonathan Ohayon `.
:mod:`sklearn.tree`
...................
- |Feature| Decision Trees can now be plotted with matplotlib using
:func:`tree.plot_tree` without relying on the ``dot`` library,
removing a hard-to-install dependency. :pr:`8508` by `Andreas Müller`_.
- |Feature| Decision Trees can now be exported in a human readable
textual format using :func:`tree.export_text`.
:pr:`6261` by `Giuseppe Vettigli `.
- |Feature| ``get_n_leaves()`` and ``get_depth()`` have been added to
:class:`tree.BaseDecisionTree` and consequently all estimators based
on it, including :class:`tree.DecisionTreeClassifier`,
:class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier`,
and :class:`tree.ExtraTreeRegressor`.
:pr:`12300` by :user:`Adrin Jalali `.
- |Fix| Trees and forests did not previously `predict` multi-output
classification targets with string labels, despite accepting them in `fit`.
:pr:`11458` by :user:`Mitar Milutinovic `.
- |Fix| Fixed an issue with :class:`tree.BaseDecisionTree`
and consequently all estimators based
on it, including :class:`tree.DecisionTreeClassifier`,
:class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier`,
and :class:`tree.ExtraTreeRegressor`, where they used to exceed the given
``max_depth`` by 1 while expanding the tree if ``max_leaf_nodes`` and
``max_depth`` were both specified by the user. Please note that this also
affects all ensemble methods using decision trees.
:pr:`12344` by :user:`Adrin Jalali `.
:mod:`sklearn.utils`
....................
- |Feature| :func:`utils.resample` now accepts a ``stratify`` parameter for
sampling according to class distributions. :pr:`13549` by :user:`Nicolas
Hug `.
- |API| Deprecated ``warn_on_dtype`` parameter from :func:`utils.check_array`
and :func:`utils.check_X_y`. Added explicit warning for dtype conversion
in :func:`check_pairwise_arrays` if the ``metric`` being passed is a
pairwise boolean metric.
:pr:`13382` by :user:`Prathmesh Savale `.
Multiple modules
................
- |MajorFeature| The `__repr__()` method of all estimators (used when calling
`print(estimator)`) has been entirely re-written, building on Python's
pretty printing standard library. All parameters are printed by default,
but this can be altered with the ``print_changed_only`` option in
:func:`sklearn.set_config`. :pr:`11705` by :user:`Nicolas Hug
`.
- |MajorFeature| Add estimators tags: these are annotations of estimators
that allow programmatic inspection of their capabilities, such as sparse
matrix support, supported output types and supported methods. Estimator
tags also determine the tests that are run on an estimator when
`check_estimator` is called. Read more in the :ref:`User Guide
`. :pr:`8022` by :user:`Andreas Müller `.
- |Efficiency| Memory copies are avoided when casting arrays to a different
dtype in multiple estimators. :pr:`11973` by :user:`Roman Yurchak
`.
- |Fix| Fixed a bug in the implementation of the :func:`our_rand_r`
helper function that was not behaving consistently across platforms.
:pr:`13422` by :user:`Madhura Parikh ` and
:user:`Clément Doumouro `.
Miscellaneous
.............
- |Enhancement| Joblib is no longer vendored in scikit-learn, and becomes a
dependency. Minimal supported version is joblib 0.11, however using
version >= 0.13 is strongly recommended.
:pr:`13531` by :user:`Roman Yurchak `.
Changes to estimator checks
---------------------------
These changes mostly affect library developers.
- Add ``check_fit_idempotent`` to
:func:`~utils.estimator_checks.check_estimator`, which checks that
when `fit` is called twice with the same data, the output of
`predict`, `predict_proba`, `transform`, and `decision_function` does not
change. :pr:`12328` by :user:`Nicolas Hug `
- Many checks can now be disabled or configured with :ref:`estimator_tags`.
:pr:`8022` by :user:`Andreas Müller `.
Code and Documentation Contributors
-----------------------------------
Thanks to everyone who has contributed to the maintenance and improvement of the
project since version 0.20, including:
adanhawth, Aditya Vyas, Adrin Jalali, Agamemnon Krasoulis, Albert Thomas,
Alberto Torres, Alexandre Gramfort, amourav, Andrea Navarrete, Andreas Mueller,
Andrew Nystrom, assiaben, Aurélien Bellet, Bartosz Michałowski, Bartosz
Telenczuk, bauks, BenjaStudio, bertrandhaut, Bharat Raghunathan, brentfagan,
Bryan Woods, Cat Chenal, Cheuk Ting Ho, Chris Choe, Christos Aridas, Clément
Doumouro, Cole Smith, Connossor, Corey Levinson, Dan Ellis, Dan Stine, Danylo
Baibak, daten-kieker, Denis Kataev, Didi Bar-Zev, Dillon Gardner, Dmitry Mottl,
Dmitry Vukolov, Dougal J. Sutherland, Dowon, drewmjohnston, Dror Atariah,
Edward J Brown, Ekaterina Krivich, Elizabeth Sander, Emmanuel Arias, Eric
Chang, Eric Larson, Erich Schubert, esvhd, Falak, Feda Curic, Federico Caselli,
Frank Hoang, Fibinse Xavier`, Finn O'Shea, Gabriel Marzinotto, Gabriel Vacaliuc,
Gabriele Calvo, Gael Varoquaux, GauravAhlawat, Giuseppe Vettigli, Greg Gandenberger,
Guillaume Fournier, Guillaume Lemaitre, Gustavo De Mari Pereira, Hanmin Qin,
haroldfox, hhu-luqi, Hunter McGushion, Ian Sanders, JackLangerman, Jacopo
Notarstefano, jakirkham, James Bourbeau, Jan Koch, Jan S, janvanrijn, Jarrod
Millman, jdethurens, jeremiedbb, JF, joaak, Joan Massich, Joel Nothman,
Jonathan Ohayon, Joris Van den Bossche, josephsalmon, Jérémie Méhault, Katrin
Leinweber, ken, kms15, Koen, Kossori Aruku, Krishna Sangeeth, Kuai Yu, Kulbear,
Kushal Chauhan, Kyle Jackson, Lakshya KD, Leandro Hermida, Lee Yi Jie Joel,
Lily Xiong, Lisa Sarah Thomas, Loic Esteve, louib, luk-f-a, maikia, mail-liam,
Manimaran, Manuel López-Ibáñez, Marc Torrellas, Marco Gaido, Marco Gorelli,
MarcoGorelli, marineLM, Mark Hannel, Martin Gubri, Masstran, mathurinm, Matthew
Roeschke, Max Copeland, melsyt, mferrari3, Mickaël Schoentgen, Ming Li, Mitar,
Mohammad Aftab, Mohammed AbdelAal, Mohammed Ibraheem, Muhammad Hassaan Rafique,
mwestt, Naoya Iijima, Nicholas Smith, Nicolas Goix, Nicolas Hug, Nikolay
Shebanov, Oleksandr Pavlyk, Oliver Rausch, Olivier Grisel, Orestis, Osman, Owen
Flanagan, Paul Paczuski, Pavel Soriano, pavlos kallis, Pawel Sendyk, peay,
Peter, Peter Cock, Peter Hausamann, Peter Marko, Pierre Glaser, pierretallotte,
Pim de Haan, Piotr Szymański, Prabakaran Kumaresshan, Pradeep Reddy Raamana,
Prathmesh Savale, Pulkit Maloo, Quentin Batista, Radostin Stoyanov, Raf
Baluyot, Rajdeep Dua, Ramil Nugmanov, Raúl García Calvo, Rebekah Kim, Reshama
Shaikh, Rohan Lekhwani, Rohan Singh, Rohan Varma, Rohit Kapoor, Roman
Feldbauer, Roman Yurchak, Romuald M, Roopam Sharma, Ryan, Rüdiger Busche, Sam
Waterbury, Samuel O. Ronsin, SandroCasagrande, Scott Cole, Scott Lowe,
Sebastian Raschka, Shangwu Yao, Shivam Kotwalia, Shiyu Duan, smarie, Sriharsha
Hatwar, Stephen Hoover, Stephen Tierney, Stéphane Couvreur, surgan12,
SylvainLan, TakingItCasual, Tashay Green, thibsej, Thomas Fan, Thomas J Fan,
Thomas Moreau, Tom Dupré la Tour, Tommy, Tulio Casagrande, Umar Farouk Umar,
Utkarsh Upadhyay, Vinayak Mehta, Vishaal Kapoor, Vivek Kumar, Vlad Niculae,
vqean3, Wenhao Zhang, William de Vazelhes, xhan, Xing Han Lu, xinyuliu12,
Yaroslav Halchenko, Zach Griffith, Zach Miller, Zayd Hammoudeh, Zhuyi Xue,
Zijie (ZJ) Poh, ^__^