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train · 16.8k rows

content large_stringlengths 3 20.5k	url large_stringlengths 54 193	branch large_stringclasses 4 values	source large_stringclasses 42 values	embeddings listlengths 384 384	score float64 -0.21 0.65
all `Array API `\_ compliant inputs. See :ref:`array\_api` for more details. - \|Feature\| :func:`sklearn.utils.check\_consistent\_length` now supports Array API compatible inputs. By :user:`Stefanie Senger ` :pr:`29519` - \|Feature\| :func:`sklearn.metrics.explained\_variance\_score` and :func:`sklearn.metrics.mean\_pinball\_loss` now support Array API compatible inputs. By :user:`Virgil Chan ` :pr:`29978` - \|Feature\| :func:`sklearn.metrics.fbeta\_score`, :func:`sklearn.metrics.precision\_score` and :func:`sklearn.metrics.recall\_score` now support Array API compatible inputs. By :user:`Omar Salman ` :pr:`30395` - \|Feature\| :func:`sklearn.utils.extmath.randomized\_svd` now support Array API compatible inputs. By :user:`Connor Lane ` and :user:`Jérémie du Boisberranger `. :pr:`30819` - \|Feature\| :func:`sklearn.metrics.hamming\_loss` now support Array API compatible inputs. By :user:`Thomas Li ` :pr:`30838` - \|Feature\| :class:`preprocessing.Binarizer` now supports Array API compatible inputs. By :user:`Yaroslav Korobko `, :user:`Olivier Grisel `, and :user:`Thomas Li `. :pr:`31190` - \|Feature\| :func:`sklearn.metrics.jaccard\_score` now supports Array API compatible inputs. By :user:`Omar Salman ` :pr:`31204` - array-api-compat and array-api-extra are now vendored within the scikit-learn source. Users of the experimental array API standard support no longer need to install array-api-compat in their environment. by :user:`Lucas Colley ` :pr:`30340` Metadata routing ---------------- Refer to the :ref:`Metadata Routing User Guide ` for more details. - \|Feature\| :class:`ensemble.BaggingClassifier` and :class:`ensemble.BaggingRegressor` now support metadata routing through their `predict`, `predict\_proba`, `predict\_log\_proba` and `decision\_function` methods and pass `\\params` to the underlying estimators. By :user:`Stefanie Senger `. :pr:`30833` :mod:`sklearn.base` ------------------- - \|Enhancement\| :class:`base.BaseEstimator` now has a parameter table added to the estimators HTML representation that can be visualized with jupyter. By :user:`Guillaume Lemaitre ` and :user:`Dea María Léon ` :pr:`30763` :mod:`sklearn.calibration` -------------------------- - \|Fix\| :class:`~calibration.CalibratedClassifierCV` now raises `FutureWarning` instead of `UserWarning` when passing `cv="prefit`". By :user:`Olivier Grisel ` - :class:`~calibration.CalibratedClassifierCV` with `method="sigmoid"` no longer crashes when passing `float64`-dtyped `sample\_weight` along with a base estimator that outputs `float32`-dtyped predictions. By :user:`Olivier Grisel ` :pr:`30873` :mod:`sklearn.compose` ---------------------- - \|API\| The `force\_int\_remainder\_cols` parameter of :class:`compose.ColumnTransformer` and :func:`compose.make\_column\_transformer` is deprecated and will be removed in 1.9. It has no effect. By :user:`Jérémie du Boisberranger ` :pr:`31167` :mod:`sklearn.covariance` ------------------------- - \|Fix\| Support for ``n\_samples == n\_features`` in `sklearn.covariance.MinCovDet` has been restored. By :user:`Antony Lee `. :pr:`30483` :mod:`sklearn.datasets` ----------------------- - \|Enhancement\| New parameter ``return\_X\_y`` added to :func:`datasets.make\_classification`. The default value of the parameter does not change how the function behaves. By :user:`Success Moses ` and :user:`Adam Cooper ` :pr:`30196` :mod:`sklearn.decomposition` ---------------------------- - \|Feature\| :class:`~sklearn.decomposition.DictionaryLearning`, :class:`~sklearn.decomposition.SparseCoder` and :class:`~sklearn.decomposition.MiniBatchDictionaryLearning` now have a ``inverse\_transform`` method. By :user:`Rémi Flamary ` :pr:`30443` :mod:`sklearn.ensemble` ----------------------- - \|Feature\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` allow for more control over the validation set used for early stopping. You can now pass data to be used for validation directly to `fit` via the arguments `X\_val`, `y\_val` and `sample\_weight\_val`. By :user:`Christian Lorentzen `. :pr:`27124` - \|Fix\| :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor` validate `estimators` to make sure it is a list of tuples. By `Thomas Fan`\_. :pr:`30649` :mod:`sklearn.feature\_selection` -------------------------------- - \|Enhancement\| :class:`feature\_selection.RFECV` now gives access to the ranking and support in each iteration and cv step of feature selection. By :user:`Marie S. ` :pr:`30179` - \|Fix\| :class:`feature\_selection.SelectFromModel` now correctly works when the estimator is an instance of :class:`linear\_model.ElasticNetCV` with its `l1\_ratio` parameter being an array-like. By :user:`Vasco Pereira `. :pr:`31107` :mod:`sklearn.gaussian\_process` ------------------------------- - \|Enhancement\| :class:`gaussian\_process.GaussianProcessClassifier` now includes a `latent\_mean\_and\_variance` method that exposes the mean and the variance of the latent function, :math:`f`, used in the Laplace approximation. By :user:`Miguel González Duque ` :pr:`22227` :mod:`sklearn.inspection` ------------------------- - \|Enhancement\| Add `custom\_values` parameter in :func:`inspection.partial\_dependence`. It enables users to pass their own grid of values at which the partial dependence should be calculated. By :user:`Freddy A. Boulton ` and :user:`Stephen Pardy ` :pr:`26202` - \|Enhancement\| :class:`inspection.DecisionBoundaryDisplay` now supports plotting all classes for multi-class problems when `response\_method` is 'decision\_function', 'predict\_proba' or 'auto'. By :user:`Lucy Liu ` :pr:`29797` - \|Fix\| :func:`inspection.partial\_dependence` now raises an informative error when passing an empty list as the	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.7.rst	main	scikit-learn	[ 0.0063700005412101746, -0.04696769267320633, -0.13933444023132324, -0.015626920387148857, -0.01910579204559326, 0.0012416121317073703, 0.010208400897681713, 0.015137368813157082, -0.044970251619815826, -0.0529324971139431, -0.03460046276450157, -0.017963586375117302, -0.00440660584717989, ...	0.161276
be calculated. By :user:`Freddy A. Boulton ` and :user:`Stephen Pardy ` :pr:`26202` - \|Enhancement\| :class:`inspection.DecisionBoundaryDisplay` now supports plotting all classes for multi-class problems when `response\_method` is 'decision\_function', 'predict\_proba' or 'auto'. By :user:`Lucy Liu ` :pr:`29797` - \|Fix\| :func:`inspection.partial\_dependence` now raises an informative error when passing an empty list as the `categorical\_features` parameter. `None` should be used instead to indicate that no categorical features are present. By :user:`Pedro Lopes `. :pr:`31146` - \|API\| :func:`inspection.partial\_dependence` does no longer accept integer dtype for numerical feature columns. Explicit conversion to floating point values is now required before calling this tool (and preferably even before fitting the model to inspect). By :user:`Olivier Grisel ` :pr:`30409` :mod:`sklearn.linear\_model` --------------------------- - \|Enhancement\| :class:`linear\_model.SGDClassifier` and :class:`linear\_model.SGDRegressor` now accept `l1\_ratio=None` when `penalty` is not `"elasticnet"`. By :user:`Marc Bresson `. :pr:`30730` - \|Efficiency\| Fitting :class:`linear\_model.Lasso` and :class:`linear\_model.ElasticNet` with `fit\_intercept=True` is faster for sparse input `X` because an unnecessary re-computation of the sum of residuals is avoided. By :user:`Christian Lorentzen ` :pr:`31387` - \|Fix\| :class:`linear\_model.LogisticRegression` and :class:`linear\_model.LogisticRegressionCV` now properly pass sample weights to :func:`utils.class\_weight.compute\_class\_weight` when fit with `class\_weight="balanced"`. By :user:`Shruti Nath ` and :user:`Olivier Grisel ` :pr:`30057` - \|Fix\| Added a new parameter `tol` to :class:`linear\_model.LinearRegression` that determines the precision of the solution `coef\_` when fitting on sparse data. By :user:`Success Moses ` :pr:`30521` - \|Fix\| The update and initialization of the hyperparameters now properly handle sample weights in :class:`linear\_model.BayesianRidge`. By :user:`Antoine Baker `. :pr:`30644` - \|Fix\| :class:`linear\_model.BayesianRidge` now uses the full SVD to correctly estimate the posterior covariance matrix `sigma\_` when `n\_samples < n\_features`. By :user:`Antoine Baker ` :pr:`31094` - \|API\| The parameter `n\_alphas` has been deprecated in the following classes: :class:`linear\_model.ElasticNetCV` and :class:`linear\_model.LassoCV` and :class:`linear\_model.MultiTaskElasticNetCV` and :class:`linear\_model.MultiTaskLassoCV`, and will be removed in 1.9. The parameter `alphas` now supports both integers and array-likes, removing the need for `n\_alphas`. From now on, only `alphas` should be set to either indicate the number of alphas to automatically generate (int) or to provide a list of alphas (array-like) to test along the regularization path. By :user:`Siddharth Bansal `. :pr:`30616` - \|API\| Using the `"liblinear"` solver for multiclass classification with a one-versus-rest scheme in :class:`linear\_model.LogisticRegression` and :class:`linear\_model.LogisticRegressionCV` is deprecated and will raise an error in version 1.8. Either use a solver which supports the multinomial loss or wrap the estimator in a :class:`sklearn.multiclass.OneVsRestClassifier` to keep applying a one-versus-rest scheme. By :user:`Jérémie du Boisberranger `. :pr:`31241` :mod:`sklearn.manifold` ----------------------- - \|Enhancement\| :class:`manifold.MDS` will switch to use `n\_init=1` by default, starting from version 1.9. By :user:`Dmitry Kobak ` :pr:`31117` - \|Fix\| :class:`manifold.MDS` now correctly handles non-metric MDS. Furthermore, the returned stress value now corresponds to the returned embedding and normalized stress is now allowed for metric MDS. By :user:`Dmitry Kobak ` :pr:`30514` - \|Fix\| :class:`manifold.MDS` now uses `eps=1e-6` by default and the convergence criterion was adjusted to make sense for both metric and non-metric MDS and to follow the reference R implementation. The formula for normalized stress was adjusted to follow the original definition by Kruskal. By :user:`Dmitry Kobak ` :pr:`31117` :mod:`sklearn.metrics` ---------------------- - \|Feature\| :func:`metrics.brier\_score\_loss` implements the Brier score for multiclass classification problems and adds a `scale\_by\_half` argument. This metric is notably useful to assess both sharpness and calibration of probabilistic classifiers. See the docstrings for more details. By :user:`Varun Aggarwal `, :user:`Olivier Grisel ` and :user:`Antoine Baker `. :pr:`22046` - \|Feature\| Add class method `from\_cv\_results` to :class:`metrics.RocCurveDisplay`, which allows easy plotting of multiple ROC curves from :func:`model\_selection.cross\_validate` results. By :user:`Lucy Liu ` :pr:`30399` - \|Enhancement\| :func:`metrics.det\_curve`, :class:`metrics.DetCurveDisplay.from\_estimator`, and :class:`metrics.DetCurveDisplay.from\_estimator` now accept a `drop\_intermediate` option to drop thresholds where true positives (tp) do not change from the previous or subsequent thresholds. All points with the same tp	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.7.rst	main	scikit-learn	[ -0.015623107552528381, -0.018347807228565216, -0.040232907980680466, -0.019974596798419952, 0.03469935804605484, -0.06308142840862274, 0.00868404470384121, 0.05426107347011566, -0.10733278095722198, -0.013401041738688946, 0.028391528874635696, -0.1233653649687767, -0.043873004615306854, -0...	-0.019592
:class:`metrics.RocCurveDisplay`, which allows easy plotting of multiple ROC curves from :func:`model\_selection.cross\_validate` results. By :user:`Lucy Liu ` :pr:`30399` - \|Enhancement\| :func:`metrics.det\_curve`, :class:`metrics.DetCurveDisplay.from\_estimator`, and :class:`metrics.DetCurveDisplay.from\_estimator` now accept a `drop\_intermediate` option to drop thresholds where true positives (tp) do not change from the previous or subsequent thresholds. All points with the same tp value have the same `fnr` and thus same y coordinate in a DET curve. By :user:`Arturo Amor ` :pr:`29151` - \|Enhancement\| :func:`~metrics.class\_likelihood\_ratios` now has a `replace\_undefined\_by` param. When there is a division by zero, the metric is undefined and the set values are returned for `LR+` and `LR-`. By :user:`Stefanie Senger ` :pr:`29288` - \|Fix\| :func:`metrics.log\_loss` now raises a `ValueError` if values of `y\_true` are missing in `labels`. By :user:`Varun Aggarwal `, :user:`Olivier Grisel ` and :user:`Antoine Baker `. :pr:`22046` - \|Fix\| :func:`metrics.det\_curve` and :class:`metrics.DetCurveDisplay` now return an extra threshold at infinity where the classifier always predicts the negative class i.e. tps = fps = 0. By :user:`Arturo Amor ` :pr:`29151` - \|Fix\| :func:`~metrics.class\_likelihood\_ratios` now raises `UndefinedMetricWarning` instead of `UserWarning` when a division by zero occurs. By :user:`Stefanie Senger ` :pr:`29288` - \|Fix\| :class:`metrics.RocCurveDisplay` will no longer set a legend when `label` is `None` in both the `line\_kwargs` and the `chance\_level\_kw`. By :user:`Arturo Amor ` :pr:`29727` - \|Fix\| Additional `sample\_weight` checking has been added to :func:`metrics.mean\_absolute\_error`, :func:`metrics.mean\_pinball\_loss`, :func:`metrics.mean\_absolute\_percentage\_error`, :func:`metrics.mean\_squared\_error`, :func:`metrics.root\_mean\_squared\_error`, :func:`metrics.mean\_squared\_log\_error`, :func:`metrics.root\_mean\_squared\_log\_error`, :func:`metrics.explained\_variance\_score`, :func:`metrics.r2\_score`, :func:`metrics.mean\_tweedie\_deviance`, :func:`metrics.mean\_poisson\_deviance`, :func:`metrics.mean\_gamma\_deviance` and :func:`metrics.d2\_tweedie\_score`. `sample\_weight` can only be 1D, consistent to `y\_true` and `y\_pred` in length or a scalar. By :user:`Lucy Liu `. :pr:`30886` - \|Fix\| :func:`~metrics.d2\_log\_loss\_score` now properly handles the case when `labels` is passed and not all of the labels are present in `y\_true`. By :user:`Vassilis Margonis ` :pr:`30903` - \|Fix\| Fix :func:`metrics.adjusted\_mutual\_info\_score` numerical issue when number of classes and samples is low. By :user:`Hleb Levitski ` :pr:`31065` - \|API\| The `sparse` parameter of :func:`metrics.fowlkes\_mallows\_score` is deprecated and will be removed in 1.9. It has no effect. By :user:`Luc Rocher `. :pr:`28981` - \|API\| The `raise\_warning` parameter of :func:`metrics.class\_likelihood\_ratios` is deprecated and will be removed in 1.9. An `UndefinedMetricWarning` will always be raised in case of a division by zero. By :user:`Stefanie Senger `. :pr:`29288` - \|API\| In :meth:`sklearn.metrics.RocCurveDisplay.from\_predictions`, the argument `y\_pred` has been renamed to `y\_score` to better reflect its purpose. `y\_pred` will be removed in 1.9. By :user:`Bagus Tris Atmaja ` in :pr:`29865` :mod:`sklearn.mixture` ---------------------- - \|Feature\| Added an attribute `lower\_bounds\_` in the :class:`mixture.BaseMixture` class to save the list of lower bounds for each iteration thereby providing insights into the convergence behavior of mixture models like :class:`mixture.GaussianMixture`. By :user:`Manideep Yenugula ` :pr:`28559` - \|Efficiency\| Simplified redundant computation when estimating covariances in :class:`~mixture.GaussianMixture` with a `covariance\_type="spherical"` or `covariance\_type="diag"`. By :user:`Leonce Mekinda ` and :user:`Olivier Grisel ` :pr:`30414` - \|Efficiency\| :class:`~mixture.GaussianMixture` now consistently operates at `float32` precision when fitted with `float32` data to improve training speed and memory efficiency. Previously, part of the computation would be implicitly cast to `float64`. By :user:`Olivier Grisel ` and :user:`Omar Salman `. :pr:`30415` :mod:`sklearn.model\_selection` ------------------------------ - \|Fix\| Hyper-parameter optimizers such as :class:`model\_selection.GridSearchCV` now forward `sample\_weight` to the scorer even when metadata routing is not enabled. By :user:`Antoine Baker ` :pr:`30743` :mod:`sklearn.multiclass` ------------------------- - \|Fix\| The `predict\_proba` method of :class:`sklearn.multiclass.OneVsRestClassifier` now returns zero for all classes when all inner estimators never predict their positive class. By :user:`Luis M. B. Varona `, :user:`Marc Bresson `, and :user:`Jérémie du Boisberranger `. :pr:`31228` :mod:`sklearn.multioutput` -------------------------- - \|Enhancement\| The parameter `base\_estimator` has been deprecated in favour of `estimator` for :class:`multioutput.RegressorChain` and :class:`multioutput.ClassifierChain`. By :user:`Success Moses ` and :user:`dikraMasrour ` :pr:`30152` :mod:`sklearn.neural\_network` ----------------------------- - \|Feature\| Added support for `sample\_weight` in :class:`neural\_network.MLPClassifier` and :class:`neural\_network.MLPRegressor`. By :user:`Zach Shu ` and :user:`Christian Lorentzen ` :pr:`30155` -	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.7.rst	main	scikit-learn	[ -0.06097785755991936, -0.12847863137722015, -0.0284829493612051, 0.01915186271071434, 0.08746008574962616, -0.025529418140649796, 0.02286452054977417, 0.04546356946229935, 0.052082665264606476, -0.04916417971253395, 0.12022649496793747, -0.10834908485412598, -0.018249772489070892, 0.040854...	0.032958
`. :pr:`31228` :mod:`sklearn.multioutput` -------------------------- - \|Enhancement\| The parameter `base\_estimator` has been deprecated in favour of `estimator` for :class:`multioutput.RegressorChain` and :class:`multioutput.ClassifierChain`. By :user:`Success Moses ` and :user:`dikraMasrour ` :pr:`30152` :mod:`sklearn.neural\_network` ----------------------------- - \|Feature\| Added support for `sample\_weight` in :class:`neural\_network.MLPClassifier` and :class:`neural\_network.MLPRegressor`. By :user:`Zach Shu ` and :user:`Christian Lorentzen ` :pr:`30155` - \|Feature\| Added parameter for `loss` in :class:`neural\_network.MLPRegressor` with options `"squared\_error"` (default) and `"poisson"` (new). By :user:`Christian Lorentzen ` :pr:`30712` - \|Fix\| :class:`neural\_network.MLPRegressor` now raises an informative error when `early\_stopping` is set and the computed validation set is too small. By :user:`David Shumway `. :pr:`24788` :mod:`sklearn.pipeline` ----------------------- - \|Enhancement\| Expose the ``verbose\_feature\_names\_out`` argument in the :func:`pipeline.make\_union` function, allowing users to control feature name uniqueness in the :class:`pipeline.FeatureUnion`. By :user:`Abhijeetsingh Meena ` :pr:`30406` :mod:`sklearn.preprocessing` ---------------------------- - \|Enhancement\| :class:`preprocessing.KBinsDiscretizer` with `strategy="uniform"` now accepts `sample\_weight`. Additionally with `strategy="quantile"` the `quantile\_method` can now be specified (in the future `quantile\_method="averaged\_inverted\_cdf"` will become the default). By :user:`Shruti Nath ` and :user:`Olivier Grisel ` :pr:`29907` - \|Fix\| :class:`preprocessing.KBinsDiscretizer` now uses weighted resampling when sample weights are given and subsampling is used. This may change results even when not using sample weights, although in absolute and not in terms of statistical properties. By :user:`Shruti Nath ` and :user:`Jérémie du Boisberranger ` :pr:`29907` - \|Fix\| Now using ``scipy.stats.yeojohnson`` instead of our own implementation of the Yeo-Johnson transform. Fixed numerical stability (mostly overflows) of the Yeo-Johnson transform with `PowerTransformer(method="yeo-johnson")` when scipy version is `>= 1.12`. Initial PR by :user:`Xuefeng Xu ` completed by :user:`Mohamed Yaich `, :user:`Oussama Er-rabie `, :user:`Mohammed Yaslam Dlimi `, :user:`Hamza Zaroual `, :user:`Amine Hannoun ` and :user:`Sylvain Marié `. :pr:`31227` :mod:`sklearn.svm` ------------------ - \|Fix\| :class:`svm.LinearSVC` now properly passes sample weights to :func:`utils.class\_weight.compute\_class\_weight` when fit with `class\_weight="balanced"`. By :user:`Shruti Nath ` :pr:`30057` :mod:`sklearn.utils` -------------------- - \|Enhancement\| :func:`utils.multiclass.type\_of\_target` raises a warning when the number of unique classes is greater than 50% of the number of samples. This warning is raised only if `y` has more than 20 samples. By :user:`Rahil Parikh `. :pr:`26335` - \|Enhancement\| :func: `resample` now handles sample weights which allows weighted resampling. By :user:`Shruti Nath ` and :user:`Olivier Grisel ` :pr:`29907` - \|Enhancement\| :func:`utils.class\_weight.compute\_class\_weight` now properly accounts for sample weights when using strategy "balanced" to calculate class weights. By :user:`Shruti Nath ` :pr:`30057` - \|Enhancement\| Warning filters from the main process are propagated to joblib workers. By `Thomas Fan`\_ :pr:`30380` - \|Enhancement\| The private helper function :func:`utils.\_safe\_indexing` now officially supports pyarrow data. For instance, passing a pyarrow `Table` as `X` in a :class:`compose.ColumnTransformer` is now possible. By :user:`Christian Lorentzen ` :pr:`31040` - \|Fix\| In :mod:`utils.estimator\_checks` we now enforce for binary classifiers a binary `y` by taking the minimum as the negative class instead of the first element, which makes it robust to `y` shuffling. It prevents two checks from wrongly failing on binary classifiers. By :user:`Antoine Baker `. :pr:`30775` - \|Fix\| :func:`utils.extmath.randomized\_svd` and :func:`utils.extmath.randomized\_range\_finder` now validate their input array to fail early with an informative error message on invalid input. By :user:`Connor Lane `. :pr:`30819` .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.6, including: 4hm3d, Aaron Schumacher, Abhijeetsingh Meena, Acciaro Gennaro Daniele, Achraf Tasfaout, Adriano Leão, Adrien Linares, Adrin Jalali, Agriya Khetarpal, Aiden Frank, Aitsaid Azzedine Idir, ajay-sentry, Akanksha Mhadolkar, Alexandre Abraham, Alfredo Saucedo, Anderson Chaves, Andres Guzman-Ballen, Aniruddha Saha, antoinebaker, Antony Lee, Arjun S, ArthurDbrn, Arturo, Arturo Amor, ash, Ashton Powell, ayoub.agouzoul, Ayrat, Bagus Tris Atmaja, Benjamin Danek, Boney Patel, Camille Troillard, Chems Ben, Christian Lorentzen, Christian Veenhuis, Christine P. Chai, claudio, Code\_Blooded, Colas, Colin Coe, Connor Lane, Corey Farwell, Daniel Agyapong, Dan Schult, Dea María Léon, Deepak Saldanha, dependabot[bot],	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.7.rst	main	scikit-learn	[ -0.08590006083250046, -0.04463157802820206, -0.04482663422822952, 0.022661153227090836, 0.047849979251623154, 0.008427475579082966, -0.017726151272654533, 0.028433825820684433, -0.04072285071015358, -0.025726260617375374, -0.002322626765817404, -0.0800381526350975, 0.0351518951356411, -0.1...	0.117866
Antony Lee, Arjun S, ArthurDbrn, Arturo, Arturo Amor, ash, Ashton Powell, ayoub.agouzoul, Ayrat, Bagus Tris Atmaja, Benjamin Danek, Boney Patel, Camille Troillard, Chems Ben, Christian Lorentzen, Christian Veenhuis, Christine P. Chai, claudio, Code\_Blooded, Colas, Colin Coe, Connor Lane, Corey Farwell, Daniel Agyapong, Dan Schult, Dea María Léon, Deepak Saldanha, dependabot[bot], Dhyey Findoriya, Dimitri Papadopoulos Orfanos, Dmitry Kobak, Domenico, elenafillo, Elham Babaei, emelia-hdz, EmilyXinyi, Emma Carballal, Eric Larson, Eugen-Bleck, Evgeni Burovski, fabianhenning, Gael Varoquaux, GaetandeCast, Gil Ramot, Gonçalo Guiomar, Gordon Grey, Goutam, G Sreeja, Guillaume Lemaitre, Haesun Park, hakan çanakçı, Hanjun Kim, Helder Geovane Gomes de Lima, Henri Bonamy, Hleb Levitski, Hugo Boulenger, IlyaSolomatin, Irene, Jérémie du Boisberranger, Jérôme Dockès, JoaoRodriguesIST, Joel Nothman, Joris Van den Bossche, Josh, jshn9515, KALLA GANASEKHAR, Kevin Klein, Krishnan Vignesh, kryggird, Loic Esteve, Lucas Colley, Luc Rocher, Lucy Liu, Luis M. B. Varona, lunovian, Mamduh Zabidi, Marc Bresson, Marco Edward Gorelli, Marco Maggi, Marek Pokropiński, Maren Westermann, Marie Sacksick, Marija Vlajic, Martin Jurča, Mayank Raj, Michael Burkhart, Miguel González Duque, Mihir Waknis, Miro Hrončok, Mohamed Ali SRIR, Mohamed DHIFALLAH, mohammed benyamna, Mohit Singh Thakur, Mounir Lbath, myenugula, Natalia Mokeeva, Nicolas Bolle, Olivier Grisel, omahs, Omar Salman, Pedro Lopes, Pedro Olivares, Peter Holzer, Prashant Bansal, Preyas Shah, Radovenchyk, Rahil Parikh, Rémi Flamary, Reshama Shaikh, Richard Harris, Rishab Saini, rolandrmgservices, SanchitD, Santiago Castro, Santiago Víquez, saskra, scikit-learn-bot, Scott Huberty, Shashank S, Shaurya Bisht, Shivam, Shruti Nath, Siddharth Bansal, SIKAI ZHANG, Simarjot Sidhu, sisird864, SiyuJin-1, Somdutta Banerjee, Sortofamudkip, sotagg, Sourabh Kumar, Stefan, Stefanie Senger, Stefano Gaspari, Steffen Rehberg, Stephen Pardy, Success Moses, Sylvain Combettes, Tahar Allouche, Thomas J. Fan, Thomas Li, ThorbenMaa, Tim Head, Tingwei Zhu, TJ Norred, Umberto Fasci, UV, Vasco Pereira, Vassilis Margonis, Velislav Babatchev, Victoria Shevchenko, viktor765, Vipsa Kamani, VirenPassi, Virgil Chan, vpz, Xiao Yuan, Yaich Mohamed, Yair Shimony, Yao Xiao, Yaroslav Halchenko, Yulia Vilensky, Yuvi Panda	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.7.rst	main	scikit-learn	[ -0.04571162909269333, -0.06223251298069954, -0.02098231203854084, 0.019915785640478134, -0.05766843631863594, 0.04934186488389969, 0.0031558596529066563, 0.03671788424253464, -0.006437242031097412, -0.009425410069525242, 0.030995123088359833, -0.013013407588005066, 0.0007204249850474298, 0...	0.118003
.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_0\_24: ============ Version 0.24 ============ For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_0\_24\_0.py`. .. include:: changelog\_legend.inc .. \_changes\_0\_24\_2: Version 0.24.2 ============== \\April 2021\\ Changelog --------- :mod:`sklearn.compose` ...................... - \|Fix\| `compose.ColumnTransformer.get\_feature\_names` does not call `get\_feature\_names` on transformers with an empty column selection. :pr:`19579` by `Thomas Fan`\_. :mod:`sklearn.cross\_decomposition` .................................. - \|Fix\| Fixed a regression in :class:`cross\_decomposition.CCA`. :pr:`19646` by `Thomas Fan`\_. - \|Fix\| :class:`cross\_decomposition.PLSRegression` raises warning for constant y residuals instead of a `StopIteration` error. :pr:`19922` by `Thomas Fan`\_. :mod:`sklearn.decomposition` ............................ - \|Fix\| Fixed a bug in :class:`decomposition.KernelPCA`'s ``inverse\_transform``. :pr:`19732` by :user:`Kei Ishikawa `. :mod:`sklearn.ensemble` ....................... - \|Fix\| Fixed a bug in :class:`ensemble.HistGradientBoostingRegressor` `fit` with `sample\_weight` parameter and `least\_absolute\_deviation` loss function. :pr:`19407` by :user:`Vadim Ushtanit `. :mod:`sklearn.feature\_extraction` ................................. - \|Fix\| Fixed a bug to support multiple strings for a category when `sparse=False` in :class:`feature\_extraction.DictVectorizer`. :pr:`19982` by :user:`Guillaume Lemaitre `. :mod:`sklearn.gaussian\_process` ............................... - \|Fix\| Avoid explicitly forming inverse covariance matrix in :class:`gaussian\_process.GaussianProcessRegressor` when set to output standard deviation. With certain covariance matrices this inverse is unstable to compute explicitly. Calling Cholesky solver mitigates this issue in computation. :pr:`19939` by :user:`Ian Halvic `. - \|Fix\| Avoid division by zero when scaling constant target in :class:`gaussian\_process.GaussianProcessRegressor`. It was due to a std. dev. equal to 0. Now, such case is detected and the std. dev. is affected to 1 avoiding a division by zero and thus the presence of NaN values in the normalized target. :pr:`19703` by :user:`sobkevich`, :user:`Boris Villazón-Terrazas ` and :user:`Alexandr Fonari `. :mod:`sklearn.linear\_model` ........................... - \|Fix\|: Fixed a bug in :class:`linear\_model.LogisticRegression`: the sample\_weight object is not modified anymore. :pr:`19182` by :user:`Yosuke KOBAYASHI `. :mod:`sklearn.metrics` ...................... - \|Fix\| :func:`metrics.top\_k\_accuracy\_score` now supports multiclass problems where only two classes appear in `y\_true` and all the classes are specified in `labels`. :pr:`19721` by :user:`Joris Clement `. :mod:`sklearn.model\_selection` .............................. - \|Fix\| :class:`model\_selection.RandomizedSearchCV` and :class:`model\_selection.GridSearchCV` now correctly show the score for single metrics and verbose > 2. :pr:`19659` by `Thomas Fan`\_. - \|Fix\| Some values in the `cv\_results\_` attribute of :class:`model\_selection.HalvingRandomSearchCV` and :class:`model\_selection.HalvingGridSearchCV` were not properly converted to numpy arrays. :pr:`19211` by `Nicolas Hug`\_. - \|Fix\| The `fit` method of the successive halving parameter search (:class:`model\_selection.HalvingGridSearchCV`, and :class:`model\_selection.HalvingRandomSearchCV`) now correctly handles the `groups` parameter. :pr:`19847` by :user:`Xiaoyu Chai `. :mod:`sklearn.multioutput` .......................... - \|Fix\| :class:`multioutput.MultiOutputRegressor` now works with estimators that dynamically define `predict` during fitting, such as :class:`ensemble.StackingRegressor`. :pr:`19308` by `Thomas Fan`\_. :mod:`sklearn.preprocessing` ............................ - \|Fix\| Validate the constructor parameter `handle\_unknown` in :class:`preprocessing.OrdinalEncoder` to only allow for `'error'` and `'use\_encoded\_value'` strategies. :pr:`19234` by `Guillaume Lemaitre `. - \|Fix\| Fix encoder categories having dtype='S' :class:`preprocessing.OneHotEncoder` and :class:`preprocessing.OrdinalEncoder`. :pr:`19727` by :user:`Andrew Delong `. - \|Fix\| :meth:`preprocessing.OrdinalEncoder.transform` correctly handles unknown values for string dtypes. :pr:`19888` by `Thomas Fan`\_. - \|Fix\| :meth:`preprocessing.OneHotEncoder.fit` no longer alters the `drop` parameter. :pr:`19924` by `Thomas Fan`\_. :mod:`sklearn.semi\_supervised` .............................. - \|Fix\| Avoid NaN during label propagation in :class:`~sklearn.semi\_supervised.LabelPropagation`. :pr:`19271` by :user:`Zhaowei Wang `. :mod:`sklearn.tree` ................... - \|Fix\| Fix a bug in `fit` of `tree.BaseDecisionTree` that caused segmentation faults under certain conditions. `fit` now deep copies the `Criterion` object to prevent shared concurrent accesses. :pr:`19580` by :user:`Samuel Brice ` and :user:`Alex Adamson ` and :user:`Wil Yegelwel `. :mod:`sklearn.utils` .................... - \|Fix\| Better contains the CSS provided by :func:`utils.estimator\_html\_repr` by giving CSS ids to the html representation. :pr:`19417` by `Thomas Fan`\_. .. \_changes\_0\_24\_1: Version 0.24.1 ============== \\January 2021\\ Packaging --------- The 0.24.0 scikit-learn wheels were not working with MacOS <1.15 due to `libomp`. The version of `libomp` used to build the wheels was too recent for older macOS versions. This issue has been fixed for 0.24.1 scikit-learn wheels. Scikit-learn wheels published on PyPI.org now officially support macOS	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.24.rst	main	scikit-learn	[ -0.023945560678839684, -0.03372732177376747, 0.001139216823503375, -0.03722098097205162, 0.07959848642349243, 0.04013044014573097, 0.015299119055271149, 0.03209578990936279, -0.03340160474181175, 0.01574208214879036, 0.08926844596862793, -0.040161196142435074, -0.05289125815033913, -0.0301...	0.037192
2021\\ Packaging --------- The 0.24.0 scikit-learn wheels were not working with MacOS <1.15 due to `libomp`. The version of `libomp` used to build the wheels was too recent for older macOS versions. This issue has been fixed for 0.24.1 scikit-learn wheels. Scikit-learn wheels published on PyPI.org now officially support macOS 10.13 and later. Changelog --------- :mod:`sklearn.metrics` ...................... - \|Fix\| Fix numerical stability bug that could happen in :func:`metrics.adjusted\_mutual\_info\_score` and :func:`metrics.mutual\_info\_score` with NumPy 1.20+. :pr:`19179` by `Thomas Fan`\_. :mod:`sklearn.semi\_supervised` .............................. - \|Fix\| :class:`semi\_supervised.SelfTrainingClassifier` is now accepting meta-estimator (e.g. :class:`ensemble.StackingClassifier`). The validation of this estimator is done on the fitted estimator, once we know the existence of the method `predict\_proba`. :pr:`19126` by :user:`Guillaume Lemaitre `. .. \_changes\_0\_24: Version 0.24.0 ============== \\December 2020\\ 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. - \|Fix\| :class:`decomposition.KernelPCA` behaviour is now more consistent between 32-bits and 64-bits data when the kernel has small positive eigenvalues. - \|Fix\| :class:`decomposition.TruncatedSVD` becomes deterministic by exposing a `random\_state` parameter. - \|Fix\| :class:`linear\_model.Perceptron` when `penalty='elasticnet'`. - \|Fix\| Change in the random sampling procedures for the center initialization of :class:`cluster.KMeans`. 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.) Changelog --------- :mod:`sklearn.base` ................... - \|Fix\| :meth:`base.BaseEstimator.get\_params` now will raise an `AttributeError` if a parameter cannot be retrieved as an instance attribute. Previously it would return `None`. :pr:`17448` by :user:`Juan Carlos Alfaro Jiménez `. :mod:`sklearn.calibration` .......................... - \|Efficiency\| :class:`calibration.CalibratedClassifierCV.fit` now supports parallelization via `joblib.Parallel` using argument `n\_jobs`. :pr:`17107` by :user:`Julien Jerphanion `. - \|Enhancement\| Allow :class:`calibration.CalibratedClassifierCV` use with prefit :class:`pipeline.Pipeline` where data is not `X` is not array-like, sparse matrix or dataframe at the start. :pr:`17546` by :user:`Lucy Liu `. - \|Enhancement\| Add `ensemble` parameter to :class:`calibration.CalibratedClassifierCV`, which enables implementation of calibration via an ensemble of calibrators (current method) or just one calibrator using all the data (similar to the built-in feature of :mod:`sklearn.svm` estimators with the `probabilities=True` parameter). :pr:`17856` by :user:`Lucy Liu ` and :user:`Andrea Esuli `. :mod:`sklearn.cluster` ...................... - \|Enhancement\| :class:`cluster.AgglomerativeClustering` has a new parameter `compute\_distances`. When set to `True`, distances between clusters are computed and stored in the `distances\_` attribute even when the parameter `distance\_threshold` is not used. This new parameter is useful to produce dendrogram visualizations, but introduces a computational and memory overhead. :pr:`17984` by :user:`Michael Riedmann `, :user:`Emilie Delattre `, and :user:`Francesco Casalegno `. - \|Enhancement\| :class:`cluster.SpectralClustering` and :func:`cluster.spectral\_clustering` have a new keyword argument `verbose`. When set to `True`, additional messages will be displayed which can aid with debugging. :pr:`18052` by :user:`Sean O. Stalley `. - \|Enhancement\| Added :func:`cluster.kmeans\_plusplus` as public function. Initialization by KMeans++ can now be called separately to generate initial cluster centroids. :pr:`17937` by :user:`g-walsh` - \|API\| :class:`cluster.MiniBatchKMeans` attributes, `counts\_` and `init\_size\_`, are deprecated and will be removed in 1.1 (renaming of 0.26). :pr:`17864` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.compose` ...................... - \|Fix\| :class:`compose.ColumnTransformer` will skip transformers the column selector is a list of bools that are False. :pr:`17616` by `Thomas Fan`\_. - \|Fix\| :class:`compose.ColumnTransformer` now displays the remainder in the diagram display. :pr:`18167` by `Thomas Fan`\_. - \|Fix\| :class:`compose.ColumnTransformer` enforces strict count and order of column names between `fit` and `transform` by raising an error instead of a warning, following the deprecation cycle. :pr:`18256` by :user:`Madhura Jayratne `. :mod:`sklearn.covariance` ......................... - \|API\| Deprecates `cv\_alphas\_` in favor of `cv\_results\_['alphas']` and `grid\_scores\_` in favor of split scores in `cv\_results\_` in :class:`covariance.GraphicalLassoCV`. `cv\_alphas\_` and `grid\_scores\_`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.24.rst	main	scikit-learn	[ -0.09516268223524094, -0.07310065627098083, -0.03571266308426857, -0.02076602354645729, 0.08950493484735489, -0.0826018825173378, -0.04102271795272827, 0.006766170728951693, -0.07195129990577698, 0.006627910770475864, 0.09435148537158966, -0.025554215535521507, -0.016111182048916817, -0.02...	0.078427
and order of column names between `fit` and `transform` by raising an error instead of a warning, following the deprecation cycle. :pr:`18256` by :user:`Madhura Jayratne `. :mod:`sklearn.covariance` ......................... - \|API\| Deprecates `cv\_alphas\_` in favor of `cv\_results\_['alphas']` and `grid\_scores\_` in favor of split scores in `cv\_results\_` in :class:`covariance.GraphicalLassoCV`. `cv\_alphas\_` and `grid\_scores\_` will be removed in version 1.1 (renaming of 0.26). :pr:`16392` by `Thomas Fan`\_. :mod:`sklearn.cross\_decomposition` .................................. - \|Fix\| Fixed a bug in :class:`cross\_decomposition.PLSSVD` which would sometimes return components in the reversed order of importance. :pr:`17095` by `Nicolas Hug`\_. - \|Fix\| Fixed a bug in :class:`cross\_decomposition.PLSSVD`, :class:`cross\_decomposition.CCA`, and :class:`cross\_decomposition.PLSCanonical`, which would lead to incorrect predictions for `est.transform(Y)` when the training data is single-target. :pr:`17095` by `Nicolas Hug`\_. - \|Fix\| Increases the stability of :class:`cross\_decomposition.CCA` :pr:`18746` by `Thomas Fan`\_. - \|API\| The bounds of the `n\_components` parameter is now restricted: - into `[1, min(n\_samples, n\_features, n\_targets)]`, for :class:`cross\_decomposition.PLSSVD`, :class:`cross\_decomposition.CCA`, and :class:`cross\_decomposition.PLSCanonical`. - into `[1, n\_features]` or :class:`cross\_decomposition.PLSRegression`. An error will be raised in 1.1 (renaming of 0.26). :pr:`17095` by `Nicolas Hug`\_. - \|API\| For :class:`cross\_decomposition.PLSSVD`, :class:`cross\_decomposition.CCA`, and :class:`cross\_decomposition.PLSCanonical`, the `x\_scores\_` and `y\_scores\_` attributes were deprecated and will be removed in 1.1 (renaming of 0.26). They can be retrieved by calling `transform` on the training data. The `norm\_y\_weights` attribute will also be removed. :pr:`17095` by `Nicolas Hug`\_. - \|API\| For :class:`cross\_decomposition.PLSRegression`, :class:`cross\_decomposition.PLSCanonical`, :class:`cross\_decomposition.CCA`, and :class:`cross\_decomposition.PLSSVD`, the `x\_mean\_`, `y\_mean\_`, `x\_std\_`, and `y\_std\_` attributes were deprecated and will be removed in 1.1 (renaming of 0.26). :pr:`18768` by :user:`Maren Westermann `. - \|Fix\| :class:`decomposition.TruncatedSVD` becomes deterministic by using the `random\_state`. It controls the weights' initialization of the underlying ARPACK solver. :pr:` #18302` by :user:`Gaurav Desai ` and :user:`Ivan Panico `. :mod:`sklearn.datasets` ....................... - \|Feature\| :func:`datasets.fetch\_openml` now validates md5 checksum of arff files downloaded or cached to ensure data integrity. :pr:`14800` by :user:`Shashank Singh ` and `Joel Nothman`\_. - \|Enhancement\| :func:`datasets.fetch\_openml` now allows argument `as\_frame` to be 'auto', which tries to convert returned data to pandas DataFrame unless data is sparse. :pr:`17396` by :user:`Jiaxiang `. - \|Enhancement\| :func:`datasets.fetch\_covtype` now supports the optional argument `as\_frame`; when it is set to True, the returned Bunch object's `data` and `frame` members are pandas DataFrames, and the `target` member is a pandas Series. :pr:`17491` by :user:`Alex Liang `. - \|Enhancement\| :func:`datasets.fetch\_kddcup99` now supports the optional argument `as\_frame`; when it is set to True, the returned Bunch object's `data` and `frame` members are pandas DataFrames, and the `target` member is a pandas Series. :pr:`18280` by :user:`Alex Liang ` and `Guillaume Lemaitre`\_. - \|Enhancement\| :func:`datasets.fetch\_20newsgroups\_vectorized` now supports loading as a pandas ``DataFrame`` by setting ``as\_frame=True``. :pr:`17499` by :user:`Brigitta Sipőcz ` and `Guillaume Lemaitre`\_. - \|API\| The default value of `as\_frame` in :func:`datasets.fetch\_openml` is changed from False to 'auto'. :pr:`17610` by :user:`Jiaxiang `. :mod:`sklearn.decomposition` ............................ - \|API\| For :class:`decomposition.NMF`, the `init` value, when 'init=None' and n\_components <= min(n\_samples, n\_features) will be changed from `'nndsvd'` to `'nndsvda'` in 1.1 (renaming of 0.26). :pr:`18525` by :user:`Chiara Marmo `. - \|Enhancement\| :func:`decomposition.FactorAnalysis` now supports the optional argument `rotation`, which can take the value `None`, `'varimax'` or `'quartimax'`. :pr:`11064` by :user:`Jona Sassenhagen `. - \|Enhancement\| :class:`decomposition.NMF` now supports the optional parameter `regularization`, which can take the values `None`, 'components', 'transformation' or 'both', in accordance with `decomposition.NMF.non\_negative\_factorization`. :pr:`17414` by :user:`Bharat Raghunathan `. - \|Fix\| :class:`decomposition.KernelPCA` behaviour is now more consistent between 32-bits and 64-bits data input when the kernel has small positive eigenvalues. Small positive eigenvalues were not correctly discarded for 32-bits data. :pr:`18149` by :user:`Sylvain Marié `. - \|Fix\| Fix :class:`decomposition.SparseCoder` such that it follows scikit-learn API and supports cloning. The attribute `components\_` is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). This attribute was	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.24.rst	main	scikit-learn	[ -0.07227489352226257, 0.025254853069782257, -0.08397308737039566, -0.014259373769164085, 0.05186289921402931, -0.033895447850227356, -0.011961067095398903, 0.006250433623790741, -0.027154654264450073, 0.022263258695602417, 0.10051535069942474, 0.02721530757844448, 0.03578052669763565, -0.0...	0.021234
small positive eigenvalues. Small positive eigenvalues were not correctly discarded for 32-bits data. :pr:`18149` by :user:`Sylvain Marié `. - \|Fix\| Fix :class:`decomposition.SparseCoder` such that it follows scikit-learn API and supports cloning. The attribute `components\_` is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). This attribute was redundant with the `dictionary` attribute and constructor parameter. :pr:`17679` by :user:`Xavier Dupré `. - \|Fix\| :meth:`decomposition.TruncatedSVD.fit\_transform` consistently returns the same as :meth:`decomposition.TruncatedSVD.fit` followed by :meth:`decomposition.TruncatedSVD.transform`. :pr:`18528` by :user:`Albert Villanova del Moral ` and :user:`Ruifeng Zheng `. :mod:`sklearn.discriminant\_analysis` .................................... - \|Enhancement\| :class:`discriminant\_analysis.LinearDiscriminantAnalysis` can now use custom covariance estimate by setting the `covariance\_estimator` parameter. :pr:`14446` by :user:`Hugo Richard `. :mod:`sklearn.ensemble` ....................... - \|MajorFeature\| :class:`ensemble.HistGradientBoostingRegressor` and :class:`ensemble.HistGradientBoostingClassifier` now have native support for categorical features with the `categorical\_features` parameter. :pr:`18394` by `Nicolas Hug`\_ and `Thomas Fan`\_. - \|Feature\| :class:`ensemble.HistGradientBoostingRegressor` and :class:`ensemble.HistGradientBoostingClassifier` now support the method `staged\_predict`, which allows monitoring of each stage. :pr:`16985` by :user:`Hao Chun Chang `. - \|Efficiency\| break cyclic references in the tree nodes used internally in :class:`ensemble.HistGradientBoostingRegressor` and :class:`ensemble.HistGradientBoostingClassifier` to allow for the timely garbage collection of large intermediate datastructures and to improve memory usage in `fit`. :pr:`18334` by `Olivier Grisel`\_ `Nicolas Hug`\_, `Thomas Fan`\_ and `Andreas Müller`\_. - \|Efficiency\| Histogram initialization is now done in parallel in :class:`ensemble.HistGradientBoostingRegressor` and :class:`ensemble.HistGradientBoostingClassifier` which results in speed improvement for problems that build a lot of nodes on multicore machines. :pr:`18341` by `Olivier Grisel`\_, `Nicolas Hug`\_, `Thomas Fan`\_, and :user:`Egor Smirnov `. - \|Fix\| Fixed a bug in :class:`ensemble.HistGradientBoostingRegressor` and :class:`ensemble.HistGradientBoostingClassifier` which can now accept data with `uint8` dtype in `predict`. :pr:`18410` by `Nicolas Hug`\_. - \|API\| The parameter ``n\_classes\_`` is now deprecated in :class:`ensemble.GradientBoostingRegressor` and returns `1`. :pr:`17702` by :user:`Simona Maggio `. - \|API\| Mean absolute error ('mae') is now deprecated for the parameter ``criterion`` in :class:`ensemble.GradientBoostingRegressor` and :class:`ensemble.GradientBoostingClassifier`. :pr:`18326` by :user:`Madhura Jayaratne `. :mod:`sklearn.exceptions` ......................... - \|API\| `exceptions.ChangedBehaviorWarning` and `exceptions.NonBLASDotWarning` are deprecated and will be removed in 1.1 (renaming of 0.26). :pr:`17804` by `Adrin Jalali`\_. :mod:`sklearn.feature\_extraction` ................................. - \|Enhancement\| :class:`feature\_extraction.DictVectorizer` accepts multiple values for one categorical feature. :pr:`17367` by :user:`Peng Yu ` and :user:`Chiara Marmo `. - \|Fix\| :class:`feature\_extraction.text.CountVectorizer` raises an issue if a custom token pattern which captures more than one group is provided. :pr:`15427` by :user:`Gangesh Gudmalwar ` and :user:`Erin R Hoffman `. :mod:`sklearn.feature\_selection` ................................ - \|Feature\| Added :class:`feature\_selection.SequentialFeatureSelector` which implements forward and backward sequential feature selection. :pr:`6545` by `Sebastian Raschka`\_ and :pr:`17159` by `Nicolas Hug`\_. - \|Feature\| A new parameter `importance\_getter` was added to :class:`feature\_selection.RFE`, :class:`feature\_selection.RFECV` and :class:`feature\_selection.SelectFromModel`, allowing the user to specify an attribute name/path or a `callable` for extracting feature importance from the estimator. :pr:`15361` by :user:`Venkatachalam N `. - \|Efficiency\| Reduce memory footprint in :func:`feature\_selection.mutual\_info\_classif` and :func:`feature\_selection.mutual\_info\_regression` by calling :class:`neighbors.KDTree` for counting nearest neighbors. :pr:`17878` by :user:`Noel Rogers `. - \|Enhancement\| :class:`feature\_selection.RFE` supports the option for the number of `n\_features\_to\_select` to be given as a float representing the percentage of features to select. :pr:`17090` by :user:`Lisa Schwetlick ` and :user:`Marija Vlajic Wheeler `. :mod:`sklearn.gaussian\_process` ............................... - \|Enhancement\| A new method `gaussian\_process.kernel.\_check\_bounds\_params` is called after fitting a Gaussian Process and raises a ``ConvergenceWarning`` if the bounds of the hyperparameters are too tight. :issue:`12638` by :user:`Sylvain Lannuzel `. :mod:`sklearn.impute` ..................... - \|Feature\| :class:`impute.SimpleImputer` now supports a list of strings when ``strategy='most\_frequent'`` or ``strategy='constant'``. :pr:`17526` by :user:`Ayako YAGI ` and :user:`Juan Carlos Alfaro Jiménez `. - \|Feature\| Added method :meth:`impute.SimpleImputer.inverse\_transform` to revert imputed data to original when instantiated with ``add\_indicator=True``. :pr:`17612` by :user:`Srimukh Sripada `. - \|Fix\| replace the default values in :class:`impute.IterativeImputer` of `min\_value` and `max\_value` parameters to `-np.inf` and `np.inf`, respectively instead of `None`. However, the behaviour of the class does not change since `None` was	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.24.rst	main	scikit-learn	[ -0.06794153153896332, 0.006026276852935553, -0.09362209588289261, -0.04804379865527153, -0.015269728377461433, -0.12878070771694183, -0.07609359920024872, 0.008230481296777725, -0.059041816741228104, -0.0015911917435005307, 0.13353054225444794, -0.021020591259002686, -0.0254924688488245, -...	-0.034
method :meth:`impute.SimpleImputer.inverse\_transform` to revert imputed data to original when instantiated with ``add\_indicator=True``. :pr:`17612` by :user:`Srimukh Sripada `. - \|Fix\| replace the default values in :class:`impute.IterativeImputer` of `min\_value` and `max\_value` parameters to `-np.inf` and `np.inf`, respectively instead of `None`. However, the behaviour of the class does not change since `None` was defaulting to these values already. :pr:`16493` by :user:`Darshan N `. - \|Fix\| :class:`impute.IterativeImputer` will not attempt to set the estimator's `random\_state` attribute, allowing to use it with more external classes. :pr:`15636` by :user:`David Cortes `. - \|Efficiency\| :class:`impute.SimpleImputer` is now faster with `object` dtype array. when `strategy='most\_frequent'` in :class:`~sklearn.impute.SimpleImputer`. :pr:`18987` by :user:`David Katz `. :mod:`sklearn.inspection` ......................... - \|Feature\| :func:`inspection.partial\_dependence` and `inspection.plot\_partial\_dependence` now support calculating and plotting Individual Conditional Expectation (ICE) curves controlled by the ``kind`` parameter. :pr:`16619` by :user:`Madhura Jayratne `. - \|Feature\| Add `sample\_weight` parameter to :func:`inspection.permutation\_importance`. :pr:`16906` by :user:`Roei Kahny `. - \|API\| Positional arguments are deprecated in :meth:`inspection.PartialDependenceDisplay.plot` and will error in 1.1 (renaming of 0.26). :pr:`18293` by `Thomas Fan`\_. :mod:`sklearn.isotonic` ....................... - \|Feature\| Expose fitted attributes ``X\_thresholds\_`` and ``y\_thresholds\_`` that hold the de-duplicated interpolation thresholds of an :class:`isotonic.IsotonicRegression` instance for model inspection purpose. :pr:`16289` by :user:`Masashi Kishimoto ` and :user:`Olivier Grisel `. - \|Enhancement\| :class:`isotonic.IsotonicRegression` now accepts 2d array with 1 feature as input array. :pr:`17379` by :user:`Jiaxiang `. - \|Fix\| Add tolerance when determining duplicate X values to prevent inf values from being predicted by :class:`isotonic.IsotonicRegression`. :pr:`18639` by :user:`Lucy Liu `. :mod:`sklearn.kernel\_approximation` ................................... - \|Feature\| Added class :class:`kernel\_approximation.PolynomialCountSketch` which implements the Tensor Sketch algorithm for polynomial kernel feature map approximation. :pr:`13003` by :user:`Daniel López Sánchez `. - \|Efficiency\| :class:`kernel\_approximation.Nystroem` now supports parallelization via `joblib.Parallel` using argument `n\_jobs`. :pr:`18545` by :user:`Laurenz Reitsam `. :mod:`sklearn.linear\_model` ........................... - \|Feature\| :class:`linear\_model.LinearRegression` now forces coefficients to be all positive when ``positive`` is set to ``True``. :pr:`17578` by :user:`Joseph Knox `, :user:`Nelle Varoquaux ` and :user:`Chiara Marmo `. - \|Enhancement\| :class:`linear\_model.RidgeCV` now supports finding an optimal regularization value `alpha` for each target separately by setting ``alpha\_per\_target=True``. This is only supported when using the default efficient leave-one-out cross-validation scheme ``cv=None``. :pr:`6624` by :user:`Marijn van Vliet `. - \|Fix\| Fixes bug in :class:`linear\_model.TheilSenRegressor` where `predict` and `score` would fail when `fit\_intercept=False` and there was one feature during fitting. :pr:`18121` by `Thomas Fan`\_. - \|Fix\| Fixes bug in :class:`linear\_model.ARDRegression` where `predict` was raising an error when `normalize=True` and `return\_std=True` because `X\_offset\_` and `X\_scale\_` were undefined. :pr:`18607` by :user:`fhaselbeck `. - \|Fix\| Added the missing `l1\_ratio` parameter in :class:`linear\_model.Perceptron`, to be used when `penalty='elasticnet'`. This changes the default from 0 to 0.15. :pr:`18622` by :user:`Haesun Park `. :mod:`sklearn.manifold` ....................... - \|Efficiency\| Fixed :issue:`10493`. Improve Local Linear Embedding (LLE) that raised `MemoryError` exception when used with large inputs. :pr:`17997` by :user:`Bertrand Maisonneuve `. - \|Enhancement\| Add `square\_distances` parameter to :class:`manifold.TSNE`, which provides backward compatibility during deprecation of legacy squaring behavior. Distances will be squared by default in 1.1 (renaming of 0.26), and this parameter will be removed in 1.3. :pr:`17662` by :user:`Joshua Newton `. - \|Fix\| :class:`manifold.MDS` now correctly sets its `\_pairwise` attribute. :pr:`18278` by `Thomas Fan`\_. :mod:`sklearn.metrics` ...................... - \|Feature\| Added :func:`metrics.cluster.pair\_confusion\_matrix` implementing the confusion matrix arising from pairs of elements from two clusterings. :pr:`17412` by :user:`Uwe F Mayer `. - \|Feature\| new metric :func:`metrics.top\_k\_accuracy\_score`. It's a generalization of :func:`metrics.top\_k\_accuracy\_score`, the difference is that a prediction is considered correct as long as the true label is associated with one of the `k` highest predicted scores. :func:`metrics.accuracy\_score` is the special case of `k = 1`. :pr:`16625` by :user:`Geoffrey Bolmier `. - \|Feature\| Added :func:`metrics.det\_curve` to compute Detection Error Tradeoff curve classification metric. :pr:`10591` by :user:`Jeremy Karnowski ` and :user:`Daniel Mohns `. - \|Feature\| Added `metrics.plot\_det\_curve` and	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.24.rst	main	scikit-learn	[ -0.0785471573472023, 0.013455376029014587, -0.033129092305898666, 0.008710706606507301, 0.012155996635556221, -0.11504679918289185, 0.010951798409223557, 0.0603659525513649, -0.04637764021754265, 0.037809792906045914, 0.003977831918746233, 0.007381401490420103, 0.03865673020482063, -0.0852...	0.087384
is associated with one of the `k` highest predicted scores. :func:`metrics.accuracy\_score` is the special case of `k = 1`. :pr:`16625` by :user:`Geoffrey Bolmier `. - \|Feature\| Added :func:`metrics.det\_curve` to compute Detection Error Tradeoff curve classification metric. :pr:`10591` by :user:`Jeremy Karnowski ` and :user:`Daniel Mohns `. - \|Feature\| Added `metrics.plot\_det\_curve` and :class:`metrics.DetCurveDisplay` to ease the plot of DET curves. :pr:`18176` by :user:`Guillaume Lemaitre `. - \|Feature\| Added :func:`metrics.mean\_absolute\_percentage\_error` metric and the associated scorer for regression problems. :issue:`10708` fixed with the PR :pr:`15007` by :user:`Ashutosh Hathidara `. The scorer and some practical test cases were taken from PR :pr:`10711` by :user:`Mohamed Ali Jamaoui `. - \|Feature\| Added :func:`metrics.rand\_score` implementing the (unadjusted) Rand index. :pr:`17412` by :user:`Uwe F Mayer `. - \|Feature\| `metrics.plot\_confusion\_matrix` now supports making colorbar optional in the matplotlib plot by setting `colorbar=False`. :pr:`17192` by :user:`Avi Gupta ` - \|Enhancement\| Add `sample\_weight` parameter to :func:`metrics.median\_absolute\_error`. :pr:`17225` by :user:`Lucy Liu `. - \|Enhancement\| Add `pos\_label` parameter in `metrics.plot\_precision\_recall\_curve` in order to specify the positive class to be used when computing the precision and recall statistics. :pr:`17569` by :user:`Guillaume Lemaitre `. - \|Enhancement\| Add `pos\_label` parameter in `metrics.plot\_roc\_curve` in order to specify the positive class to be used when computing the roc auc statistics. :pr:`17651` by :user:`Clara Matos `. - \|Fix\| Fixed a bug in :func:`metrics.classification\_report` which was raising AttributeError when called with `output\_dict=True` for 0-length values. :pr:`17777` by :user:`Shubhanshu Mishra `. - \|Fix\| Fixed a bug in :func:`metrics.classification\_report` which was raising AttributeError when called with `output\_dict=True` for 0-length values. :pr:`17777` by :user:`Shubhanshu Mishra `. - \|Fix\| Fixed a bug in :func:`metrics.jaccard\_score` which recommended the `zero\_division` parameter when called with no true or predicted samples. :pr:`17826` by :user:`Richard Decal ` and :user:`Joseph Willard ` - \|Fix\| bug in :func:`metrics.hinge\_loss` where error occurs when ``y\_true`` is missing some labels that are provided explicitly in the ``labels`` parameter. :pr:`17935` by :user:`Cary Goltermann `. - \|Fix\| Fix scorers that accept a pos\_label parameter and compute their metrics from values returned by `decision\_function` or `predict\_proba`. Previously, they would return erroneous values when pos\_label was not corresponding to `classifier.classes\_[1]`. This is especially important when training classifiers directly with string labeled target classes. :pr:`18114` by :user:`Guillaume Lemaitre `. - \|Fix\| Fixed bug in `metrics.plot\_confusion\_matrix` where error occurs when `y\_true` contains labels that were not previously seen by the classifier while the `labels` and `display\_labels` parameters are set to `None`. :pr:`18405` by :user:`Thomas J. Fan ` and :user:`Yakov Pchelintsev `. :mod:`sklearn.model\_selection` .............................. - \|MajorFeature\| Added (experimental) parameter search estimators :class:`model\_selection.HalvingRandomSearchCV` and :class:`model\_selection.HalvingGridSearchCV` which implement Successive Halving, and can be used as a drop-in replacements for :class:`model\_selection.RandomizedSearchCV` and :class:`model\_selection.GridSearchCV`. :pr:`13900` by `Nicolas Hug`\_, `Joel Nothman`\_ and `Andreas Müller`\_. - \|Feature\| :class:`model\_selection.RandomizedSearchCV` and :class:`model\_selection.GridSearchCV` now have the method ``score\_samples`` :pr:`17478` by :user:`Teon Brooks ` and :user:`Mohamed Maskani `. - \|Enhancement\| :class:`model\_selection.TimeSeriesSplit` has two new keyword arguments `test\_size` and `gap`. `test\_size` allows the out-of-sample time series length to be fixed for all folds. `gap` removes a fixed number of samples between the train and test set on each fold. :pr:`13204` by :user:`Kyle Kosic `. - \|Enhancement\| :func:`model\_selection.permutation\_test\_score` and :func:`model\_selection.validation\_curve` now accept fit\_params to pass additional estimator parameters. :pr:`18527` by :user:`Gaurav Dhingra `, :user:`Julien Jerphanion ` and :user:`Amanda Dsouza `. - \|Enhancement\| :func:`model\_selection.cross\_val\_score`, :func:`model\_selection.cross\_validate`, :class:`model\_selection.GridSearchCV`, and :class:`model\_selection.RandomizedSearchCV` allows estimator to fail scoring and replace the score with `error\_score`. If `error\_score="raise"`, the error will be raised. :pr:`18343` by `Guillaume Lemaitre`\_ and :user:`Devi Sandeep `. - \|Enhancement\| :func:`model\_selection.learning\_curve` now accept fit\_params to pass additional estimator parameters. :pr:`18595` by :user:`Amanda Dsouza `. - \|Fix\| Fixed the `len` of :class:`model\_selection.ParameterSampler` when all distributions are lists and `n\_iter` is more than the number of unique parameter combinations. :pr:`18222` by	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.24.rst	main	scikit-learn	[ -0.06868541985750198, -0.016484344378113747, -0.087758369743824, 0.01565486192703247, 0.00408369954675436, -0.01627635583281517, 0.07409301400184631, 0.10966067761182785, -0.01208845991641283, 0.032280754297971725, 0.06648651510477066, -0.05395878851413727, 0.0563044473528862, 0.0148202953...	0.120427
raised. :pr:`18343` by `Guillaume Lemaitre`\_ and :user:`Devi Sandeep `. - \|Enhancement\| :func:`model\_selection.learning\_curve` now accept fit\_params to pass additional estimator parameters. :pr:`18595` by :user:`Amanda Dsouza `. - \|Fix\| Fixed the `len` of :class:`model\_selection.ParameterSampler` when all distributions are lists and `n\_iter` is more than the number of unique parameter combinations. :pr:`18222` by `Nicolas Hug`\_. - \|Fix\| A fix to raise warning when one or more CV splits of :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` results in non-finite scores. :pr:`18266` by :user:`Subrat Sahu `, :user:`Nirvan ` and :user:`Arthur Book `. - \|Enhancement\| :class:`model\_selection.GridSearchCV`, :class:`model\_selection.RandomizedSearchCV` and :func:`model\_selection.cross\_validate` support `scoring` being a callable returning a dictionary of multiple metric names/values association. :pr:`15126` by `Thomas Fan`\_. :mod:`sklearn.multiclass` ......................... - \|Enhancement\| :class:`multiclass.OneVsOneClassifier` now accepts the inputs with missing values. Hence, estimators which can handle missing values (may be a pipeline with imputation step) can be used as a estimator for multiclass wrappers. :pr:`17987` by :user:`Venkatachalam N `. - \|Fix\| A fix to allow :class:`multiclass.OutputCodeClassifier` to accept sparse input data in its `fit` and `predict` methods. The check for validity of the input is now delegated to the base estimator. :pr:`17233` by :user:`Zolisa Bleki `. :mod:`sklearn.multioutput` .......................... - \|Enhancement\| :class:`multioutput.MultiOutputClassifier` and :class:`multioutput.MultiOutputRegressor` now accepts the inputs with missing values. Hence, estimators which can handle missing values (may be a pipeline with imputation step, HistGradientBoosting estimators) can be used as a estimator for multiclass wrappers. :pr:`17987` by :user:`Venkatachalam N `. - \|Fix\| A fix to accept tuples for the ``order`` parameter in :class:`multioutput.ClassifierChain`. :pr:`18124` by :user:`Gus Brocchini ` and :user:`Amanda Dsouza `. :mod:`sklearn.naive\_bayes` .......................... - \|Enhancement\| Adds a parameter `min\_categories` to :class:`naive\_bayes.CategoricalNB` that allows a minimum number of categories per feature to be specified. This allows categories unseen during training to be accounted for. :pr:`16326` by :user:`George Armstrong `. - \|API\| The attributes ``coef\_`` and ``intercept\_`` are now deprecated in :class:`naive\_bayes.MultinomialNB`, :class:`naive\_bayes.ComplementNB`, :class:`naive\_bayes.BernoulliNB` and :class:`naive\_bayes.CategoricalNB`, and will be removed in v1.1 (renaming of 0.26). :pr:`17427` by :user:`Juan Carlos Alfaro Jiménez `. :mod:`sklearn.neighbors` ........................ - \|Efficiency\| Speed up ``seuclidean``, ``wminkowski``, ``mahalanobis`` and ``haversine`` metrics in `neighbors.DistanceMetric` by avoiding unexpected GIL acquiring in Cython when setting ``n\_jobs>1`` in :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.RadiusNeighborsRegressor`, :func:`metrics.pairwise\_distances` and by validating data out of loops. :pr:`17038` by :user:`Wenbo Zhao `. - \|Efficiency\| `neighbors.NeighborsBase` benefits of an improved `algorithm = 'auto'` heuristic. In addition to the previous set of rules, now, when the number of features exceeds 15, `brute` is selected, assuming the data intrinsic dimensionality is too high for tree-based methods. :pr:`17148` by :user:`Geoffrey Bolmier `. - \|Fix\| `neighbors.BinaryTree` will raise a `ValueError` when fitting on data array having points with different dimensions. :pr:`18691` by :user:`Chiara Marmo `. - \|Fix\| :class:`neighbors.NearestCentroid` with a numerical `shrink\_threshold` will raise a `ValueError` when fitting on data with all constant features. :pr:`18370` by :user:`Trevor Waite `. - \|Fix\| In methods `radius\_neighbors` and `radius\_neighbors\_graph` of :class:`neighbors.NearestNeighbors`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.RadiusNeighborsRegressor`, and :class:`neighbors.RadiusNeighborsTransformer`, using `sort\_results=True` now correctly sorts the results even when fitting with the "brute" algorithm. :pr:`18612` by `Tom Dupre la Tour`\_. :mod:`sklearn.neural\_network` ............................. - \|Efficiency\| Neural net training and prediction are now a little faster. :pr:`17603`, :pr:`17604`, :pr:`17606`, :pr:`17608`, :pr:`17609`, :pr:`17633`, :pr:`17661`, :pr:`17932` by :user:`Alex Henrie `. - \|Enhancement\| Avoid converting float32 input to float64 in :class:`neural\_network.BernoulliRBM`. :pr:`16352` by :user:`Arthur Imbert `. - \|Enhancement\| Support 32-bit computations in :class:`neural\_network.MLPClassifier` and :class:`neural\_network.MLPRegressor`. :pr:`17759` by :user:`Srimukh Sripada `. - \|Fix\| Fix method :meth:`neural\_network.MLPClassifier.fit` not iterating to ``max\_iter`` if warm started. :pr:`18269` by :user:`Norbert Preining ` and :user:`Guillaume Lemaitre `. :mod:`sklearn.pipeline` ....................... - \|Enhancement\| References to transformers passed through ``transformer\_weights`` to :class:`pipeline.FeatureUnion` that aren't present in ``transformer\_list`` will raise a ``ValueError``. :pr:`17876` by :user:`Cary Goltermann `. - \|Fix\| A slice of a :class:`pipeline.Pipeline` now inherits the parameters of	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.24.rst	main	scikit-learn	[ -0.071900874376297, -0.030093714594841003, -0.05269859358668327, 0.03682456910610199, 0.046032313257455826, 0.004261746536940336, 0.015374991111457348, 0.05840541049838066, -0.031583521515131, 0.011824678629636765, 0.032519955188035965, -0.06904371082782745, 0.04055464640259743, -0.0557809...	0.012028
if warm started. :pr:`18269` by :user:`Norbert Preining ` and :user:`Guillaume Lemaitre `. :mod:`sklearn.pipeline` ....................... - \|Enhancement\| References to transformers passed through ``transformer\_weights`` to :class:`pipeline.FeatureUnion` that aren't present in ``transformer\_list`` will raise a ``ValueError``. :pr:`17876` by :user:`Cary Goltermann `. - \|Fix\| A slice of a :class:`pipeline.Pipeline` now inherits the parameters of the original pipeline (`memory` and `verbose`). :pr:`18429` by :user:`Albert Villanova del Moral ` and :user:`Paweł Biernat `. :mod:`sklearn.preprocessing` ............................ - \|Feature\| :class:`preprocessing.OneHotEncoder` now supports missing values by treating them as a category. :pr:`17317` by `Thomas Fan`\_. - \|Feature\| Add a new ``handle\_unknown`` parameter with a ``use\_encoded\_value`` option, along with a new ``unknown\_value`` parameter, to :class:`preprocessing.OrdinalEncoder` to allow unknown categories during transform and set the encoded value of the unknown categories. :pr:`17406` by :user:`Felix Wick ` and :pr:`18406` by `Nicolas Hug`\_. - \|Feature\| Add ``clip`` parameter to :class:`preprocessing.MinMaxScaler`, which clips the transformed values of test data to ``feature\_range``. :pr:`17833` by :user:`Yashika Sharma `. - \|Feature\| Add ``sample\_weight`` parameter to :class:`preprocessing.StandardScaler`. Allows setting individual weights for each sample. :pr:`18510` and :pr:`18447` and :pr:`16066` and :pr:`18682` by :user:`Maria Telenczuk ` and :user:`Albert Villanova ` and :user:`panpiort8` and :user:`Alex Gramfort `. - \|Enhancement\| Verbose output of :class:`model\_selection.GridSearchCV` has been improved for readability. :pr:`16935` by :user:`Raghav Rajagopalan ` and :user:`Chiara Marmo `. - \|Enhancement\| Add ``unit\_variance`` to :class:`preprocessing.RobustScaler`, which scales output data such that normally distributed features have a variance of 1. :pr:`17193` by :user:`Lucy Liu ` and :user:`Mabel Villalba `. - \|Enhancement\| Add `dtype` parameter to :class:`preprocessing.KBinsDiscretizer`. :pr:`16335` by :user:`Arthur Imbert `. - \|Fix\| Raise error on :meth:`sklearn.preprocessing.OneHotEncoder.inverse\_transform` when `handle\_unknown='error'` and `drop=None` for samples encoded as all zeros. :pr:`14982` by :user:`Kevin Winata `. :mod:`sklearn.semi\_supervised` .............................. - \|MajorFeature\| Added :class:`semi\_supervised.SelfTrainingClassifier`, a meta-classifier that allows any supervised classifier to function as a semi-supervised classifier that can learn from unlabeled data. :issue:`11682` by :user:`Oliver Rausch ` and :user:`Patrice Becker `. - \|Fix\| Fix incorrect encoding when using unicode string dtypes in :class:`preprocessing.OneHotEncoder` and :class:`preprocessing.OrdinalEncoder`. :pr:`15763` by `Thomas Fan`\_. :mod:`sklearn.svm` .................. - \|Enhancement\| invoke SciPy BLAS API for SVM kernel function in ``fit``, ``predict`` and related methods of :class:`svm.SVC`, :class:`svm.NuSVC`, :class:`svm.SVR`, :class:`svm.NuSVR`, :class:`svm.OneClassSVM`. :pr:`16530` by :user:`Shuhua Fan `. :mod:`sklearn.tree` ................... - \|Feature\| :class:`tree.DecisionTreeRegressor` now supports the new splitting criterion ``'poisson'`` useful for modeling count data. :pr:`17386` by :user:`Christian Lorentzen `. - \|Enhancement\| :func:`tree.plot\_tree` now uses colors from the matplotlib configuration settings. :pr:`17187` by `Andreas Müller`\_. - \|API\| The parameter ``X\_idx\_sorted`` is now deprecated in :meth:`tree.DecisionTreeClassifier.fit` and :meth:`tree.DecisionTreeRegressor.fit`, and has no effect. :pr:`17614` by :user:`Juan Carlos Alfaro Jiménez `. :mod:`sklearn.utils` .................... - \|Enhancement\| Add ``check\_methods\_sample\_order\_invariance`` to :func:`~utils.estimator\_checks.check\_estimator`, which checks that estimator methods are invariant if applied to the same dataset with different sample order :pr:`17598` by :user:`Jason Ngo `. - \|Enhancement\| Add support for weights in `utils.sparse\_func.incr\_mean\_variance\_axis`. By :user:`Maria Telenczuk ` and :user:`Alex Gramfort `. - \|Fix\| Raise ValueError with clear error message in :func:`utils.check\_array` for sparse DataFrames with mixed types. :pr:`17992` by :user:`Thomas J. Fan ` and :user:`Alex Shacked `. - \|Fix\| Allow serialized tree based models to be unpickled on a machine with different endianness. :pr:`17644` by :user:`Qi Zhang `. - \|Fix\| Check that we raise proper error when axis=1 and the dimensions do not match in `utils.sparse\_func.incr\_mean\_variance\_axis`. By :user:`Alex Gramfort `. Miscellaneous ............. - \|Enhancement\| Calls to ``repr`` are now faster when `print\_changed\_only=True`, especially with meta-estimators. :pr:`18508` by :user:`Nathan C. `. .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.23, including: Abo7atm, Adam Spannbauer, Adrin Jalali, adrinjalali, Agamemnon Krasoulis, Akshay Deodhar, Albert Villanova del Moral, Alessandro Gentile, Alex Henrie, Alex Itkes, Alex Liang, Alexander Lenail, alexandracraciun, Alexandre Gramfort, alexshacked, Allan D Butler,	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.24.rst	main	scikit-learn	[ -0.13756997883319855, 0.040834713727235794, -0.04477424919605255, 0.00046849166392348707, -0.042451560497283936, -0.05668102204799652, -0.0105493338778615, 0.06787028908729553, -0.09842482209205627, -0.033907826989889145, 0.019861210137605667, 0.022074174135923386, 0.03565523028373718, -0....	0.113454
contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.23, including: Abo7atm, Adam Spannbauer, Adrin Jalali, adrinjalali, Agamemnon Krasoulis, Akshay Deodhar, Albert Villanova del Moral, Alessandro Gentile, Alex Henrie, Alex Itkes, Alex Liang, Alexander Lenail, alexandracraciun, Alexandre Gramfort, alexshacked, Allan D Butler, Amanda Dsouza, amy12xx, Anand Tiwari, Anderson Nelson, Andreas Mueller, Ankit Choraria, Archana Subramaniyan, Arthur Imbert, Ashutosh Hathidara, Ashutosh Kushwaha, Atsushi Nukariya, Aura Munoz, AutoViz and Auto\_ViML, Avi Gupta, Avinash Anakal, Ayako YAGI, barankarakus, barberogaston, beatrizsmg, Ben Mainye, Benjamin Bossan, Benjamin Pedigo, Bharat Raghunathan, Bhavika Devnani, Biprateep Dey, bmaisonn, Bo Chang, Boris Villazón-Terrazas, brigi, Brigitta Sipőcz, Bruno Charron, Byron Smith, Cary Goltermann, Cat Chenal, CeeThinwa, chaitanyamogal, Charles Patel, Chiara Marmo, Christian Kastner, Christian Lorentzen, Christoph Deil, Christos Aridas, Clara Matos, clmbst, Coelhudo, crispinlogan, Cristina Mulas, Daniel López, Daniel Mohns, darioka, Darshan N, david-cortes, Declan O'Neill, Deeksha Madan, Elizabeth DuPre, Eric Fiegel, Eric Larson, Erich Schubert, Erin Khoo, Erin R Hoffman, eschibli, Felix Wick, fhaselbeck, Forrest Koch, Francesco Casalegno, Frans Larsson, Gael Varoquaux, Gaurav Desai, Gaurav Sheni, genvalen, Geoffrey Bolmier, George Armstrong, George Kiragu, Gesa Stupperich, Ghislain Antony Vaillant, Gim Seng, Gordon Walsh, Gregory R. Lee, Guillaume Chevalier, Guillaume Lemaitre, Haesun Park, Hannah Bohle, Hao Chun Chang, Harry Scholes, Harsh Soni, Henry, Hirofumi Suzuki, Hitesh Somani, Hoda1394, Hugo Le Moine, hugorichard, indecisiveuser, Isuru Fernando, Ivan Wiryadi, j0rd1smit, Jaehyun Ahn, Jake Tae, James Hoctor, Jan Vesely, Jeevan Anand Anne, JeroenPeterBos, JHayes, Jiaxiang, Jie Zheng, Jigna Panchal, jim0421, Jin Li, Joaquin Vanschoren, Joel Nothman, Jona Sassenhagen, Jonathan, Jorge Gorbe Moya, Joseph Lucas, Joshua Newton, Juan Carlos Alfaro Jiménez, Julien Jerphanion, Justin Huber, Jérémie du Boisberranger, Kartik Chugh, Katarina Slama, kaylani2, Kendrick Cetina, Kenny Huynh, Kevin Markham, Kevin Winata, Kiril Isakov, kishimoto, Koki Nishihara, Krum Arnaudov, Kyle Kosic, Lauren Oldja, Laurenz Reitsam, Lisa Schwetlick, Louis Douge, Louis Guitton, Lucy Liu, Madhura Jayaratne, maikia, Manimaran, Manuel López-Ibáñez, Maren Westermann, Maria Telenczuk, Mariam-ke, Marijn van Vliet, Markus Löning, Martin Scheubrein, Martina G. Vilas, Martina Megasari, Mateusz Górski, mathschy, mathurinm, Matthias Bussonnier, Max Del Giudice, Michael, Milan Straka, Muoki Caleb, N. Haiat, Nadia Tahiri, Ph. D, Naoki Hamada, Neil Botelho, Nicolas Hug, Nils Werner, noelano, Norbert Preining, oj\_lappi, Oleh Kozynets, Olivier Grisel, Pankaj Jindal, Pardeep Singh, Parthiv Chigurupati, Patrice Becker, Pete Green, pgithubs, Poorna Kumar, Prabakaran Kumaresshan, Probinette4, pspachtholz, pwalchessen, Qi Zhang, rachel fischoff, Rachit Toshniwal, Rafey Iqbal Rahman, Rahul Jakhar, Ram Rachum, RamyaNP, rauwuckl, Ravi Kiran Boggavarapu, Ray Bell, Reshama Shaikh, Richard Decal, Rishi Advani, Rithvik Rao, Rob Romijnders, roei, Romain Tavenard, Roman Yurchak, Ruby Werman, Ryotaro Tsukada, sadak, Saket Khandelwal, Sam, Sam Ezebunandu, Sam Kimbinyi, Sarah Brown, Saurabh Jain, Sean O. Stalley, Sergio, Shail Shah, Shane Keller, Shao Yang Hong, Shashank Singh, Shooter23, Shubhanshu Mishra, simonamaggio, Soledad Galli, Srimukh Sripada, Stephan Steinfurt, subrat93, Sunitha Selvan, Swier, Sylvain Marié, SylvainLan, t-kusanagi2, Teon L Brooks, Terence Honles, Thijs van den Berg, Thomas J Fan, Thomas J. Fan, Thomas S Benjamin, Thomas9292, Thorben Jensen, tijanajovanovic, Timo Kaufmann, tnwei, Tom Dupré la Tour, Trevor Waite, ufmayer, Umberto Lupo, Venkatachalam N, Vikas Pandey, Vinicius Rios Fuck, Violeta, watchtheblur, Wenbo Zhao, willpeppo, xavier dupré, Xethan, Xue Qianming, xun-tang, yagi-3, Yakov Pchelintsev, Yashika Sharma, Yi-Yan Ge, Yue Wu, Yutaro Ikeda, Zaccharie Ramzi, zoj613, Zhao Feng.	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.24.rst	main	scikit-learn	[ -0.06180858239531517, 0.009971484541893005, 0.02956267073750496, -0.05660635232925415, 0.02095651626586914, 0.021821698173880577, -0.06855069845914841, 0.050905849784612656, -0.07324490696191788, 0.02966940402984619, -0.05211557075381279, 0.02445775829255581, -0.016461819410324097, 0.00218...	0.214242
.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_3: =========== Version 1.3 =========== For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_1\_3\_0.py`. .. include:: changelog\_legend.inc .. \_changes\_1\_3\_2: Version 1.3.2 ============= \\October 2023\\ Changelog --------- :mod:`sklearn.datasets` ....................... - \|Fix\| All dataset fetchers now accept `data\_home` as any object that implements the :class:`os.PathLike` interface, for instance, :class:`pathlib.Path`. :pr:`27468` by :user:`Yao Xiao `. :mod:`sklearn.decomposition` ............................ - \|Fix\| Fixes a bug in :class:`decomposition.KernelPCA` by forcing the output of the internal :class:`preprocessing.KernelCenterer` to be a default array. When the arpack solver is used, it expects an array with a `dtype` attribute. :pr:`27583` by :user:`Guillaume Lemaitre `. :mod:`sklearn.metrics` ...................... - \|Fix\| Fixes a bug for metrics using `zero\_division=np.nan` (e.g. :func:`~metrics.precision\_score`) within a parallel loop (e.g. :func:`~model\_selection.cross\_val\_score`) where the singleton for `np.nan` will be different in the sub-processes. :pr:`27573` by :user:`Guillaume Lemaitre `. :mod:`sklearn.tree` ................... - \|Fix\| Do not leak data via non-initialized memory in decision tree pickle files and make the generation of those files deterministic. :pr:`27580` by :user:`Loïc Estève `. .. \_changes\_1\_3\_1: Version 1.3.1 ============= \\September 2023\\ 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. - \|Fix\| Ridge models with `solver='sparse\_cg'` may have slightly different results with scipy>=1.12, because of an underlying change in the scipy solver (see `scipy#18488 `\_ for more details) :pr:`26814` by :user:`Loïc Estève ` Changes impacting all modules ----------------------------- - \|Fix\| The `set\_output` API correctly works with list input. :pr:`27044` by `Thomas Fan`\_. Changelog --------- :mod:`sklearn.calibration` .......................... - \|Fix\| :class:`calibration.CalibratedClassifierCV` can now handle models that produce large prediction scores. Before it was numerically unstable. :pr:`26913` by :user:`Omar Salman `. :mod:`sklearn.cluster` ...................... - \|Fix\| :class:`cluster.BisectingKMeans` could crash when predicting on data with a different scale than the data used to fit the model. :pr:`27167` by `Olivier Grisel`\_. - \|Fix\| :class:`cluster.BisectingKMeans` now works with data that has a single feature. :pr:`27243` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.cross\_decomposition` .................................. - \|Fix\| :class:`cross\_decomposition.PLSRegression` now automatically ravels the output of `predict` if fitted with one dimensional `y`. :pr:`26602` by :user:`Yao Xiao `. :mod:`sklearn.ensemble` ....................... - \|Fix\| Fix a bug in :class:`ensemble.AdaBoostClassifier` with `algorithm="SAMME"` where the decision function of each weak learner should be symmetric (i.e. the sum of the scores should sum to zero for a sample). :pr:`26521` by :user:`Guillaume Lemaitre `. :mod:`sklearn.feature\_selection` ................................ - \|Fix\| :func:`feature\_selection.mutual\_info\_regression` now correctly computes the result when `X` is of integer dtype. :pr:`26748` by :user:`Yao Xiao `. :mod:`sklearn.impute` ..................... - \|Fix\| :class:`impute.KNNImputer` now correctly adds a missing indicator column in ``transform`` when ``add\_indicator`` is set to ``True`` and missing values are observed during ``fit``. :pr:`26600` by :user:`Shreesha Kumar Bhat `. :mod:`sklearn.metrics` ...................... - \|Fix\| Scorers used with :func:`metrics.get\_scorer` handle properly multilabel-indicator matrix. :pr:`27002` by :user:`Guillaume Lemaitre `. :mod:`sklearn.mixture` ...................... - \|Fix\| The initialization of :class:`mixture.GaussianMixture` from user-provided `precisions\_init` for `covariance\_type` of `full` or `tied` was not correct, and has been fixed. :pr:`26416` by :user:`Yang Tao `. :mod:`sklearn.neighbors` ........................ - \|Fix\| :meth:`neighbors.KNeighborsClassifier.predict` no longer raises an exception for `pandas.DataFrames` input. :pr:`26772` by :user:`Jérémie du Boisberranger `. - \|Fix\| Reintroduce `sklearn.neighbors.BallTree.valid\_metrics` and `sklearn.neighbors.KDTree.valid\_metrics` as public class attributes. :pr:`26754` by :user:`Julien Jerphanion `. - \|Fix\| :class:`sklearn.model\_selection.HalvingRandomSearchCV` no longer raises when the input to the `param\_distributions` parameter is a list of dicts. :pr:`26893` by :user:`Stefanie Senger `. - \|Fix\| Neighbors based estimators now correctly work when `metric="minkowski"` and the metric parameter `p` is in the range `0 < p < 1`, regardless of the `dtype` of `X`. :pr:`26760` by :user:`Shreesha Kumar Bhat `. :mod:`sklearn.preprocessing`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.3.rst	main	scikit-learn	[ -0.04031049460172653, -0.027108147740364075, 0.05672025308012962, -0.037605706602334976, 0.10629136860370636, 0.008561189286410809, 0.04193402826786041, -0.025849780067801476, -0.06338836252689362, 0.03287937492132187, 0.10104278475046158, -0.015812264755368233, -0.0348903052508831, -0.047...	0.042996
the `param\_distributions` parameter is a list of dicts. :pr:`26893` by :user:`Stefanie Senger `. - \|Fix\| Neighbors based estimators now correctly work when `metric="minkowski"` and the metric parameter `p` is in the range `0 < p < 1`, regardless of the `dtype` of `X`. :pr:`26760` by :user:`Shreesha Kumar Bhat `. :mod:`sklearn.preprocessing` ............................ - \|Fix\| :class:`preprocessing.LabelEncoder` correctly accepts `y` as a keyword argument. :pr:`26940` by `Thomas Fan`\_. - \|Fix\| :class:`preprocessing.OneHotEncoder` shows a more informative error message when `sparse\_output=True` and the output is configured to be pandas. :pr:`26931` by `Thomas Fan`\_. :mod:`sklearn.tree` ................... - \|Fix\| :func:`tree.plot\_tree` now accepts `class\_names=True` as documented. :pr:`26903` by :user:`Thomas Roehr <2maz>` - \|Fix\| The `feature\_names` parameter of :func:`tree.plot\_tree` now accepts any kind of array-like instead of just a list. :pr:`27292` by :user:`Rahil Parikh `. .. \_changes\_1\_3: Version 1.3.0 ============= \\June 2023\\ 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. - \|Enhancement\| :meth:`multiclass.OutputCodeClassifier.predict` now uses a more efficient pairwise distance reduction. As a consequence, the tie-breaking strategy is different and thus the predicted labels may be different. :pr:`25196` by :user:`Guillaume Lemaitre `. - \|Enhancement\| The `fit\_transform` method of :class:`decomposition.DictionaryLearning` is more efficient but may produce different results as in previous versions when `transform\_algorithm` is not the same as `fit\_algorithm` and the number of iterations is small. :pr:`24871` by :user:`Omar Salman `. - \|Enhancement\| The `sample\_weight` parameter now will be used in centroids initialization for :class:`cluster.KMeans`, :class:`cluster.BisectingKMeans` and :class:`cluster.MiniBatchKMeans`. This change will break backward compatibility, since numbers generated from same random seeds will be different. :pr:`25752` by :user:`Hleb Levitski `, :user:`Jérémie du Boisberranger `, :user:`Guillaume Lemaitre `. - \|Fix\| Treat more consistently small values in the `W` and `H` matrices during the `fit` and `transform` steps of :class:`decomposition.NMF` and :class:`decomposition.MiniBatchNMF` which can produce different results than previous versions. :pr:`25438` by :user:`Yotam Avidar-Constantini `. - \|Fix\| :class:`decomposition.KernelPCA` may produce different results through `inverse\_transform` if `gamma` is `None`. Now it will be chosen correctly as `1/n\_features` of the data that it is fitted on, while previously it might be incorrectly chosen as `1/n\_features` of the data passed to `inverse\_transform`. A new attribute `gamma\_` is provided for revealing the actual value of `gamma` used each time the kernel is called. :pr:`26337` by :user:`Yao Xiao `. Changed displays ---------------- - \|Enhancement\| :class:`model\_selection.LearningCurveDisplay` displays both the train and test curves by default. You can set `score\_type="test"` to keep the past behaviour. :pr:`25120` by :user:`Guillaume Lemaitre `. - \|Fix\| :class:`model\_selection.ValidationCurveDisplay` now accepts passing a list to the `param\_range` parameter. :pr:`27311` by :user:`Arturo Amor `. Changes impacting all modules ----------------------------- - \|Enhancement\| The `get\_feature\_names\_out` method of the following classes now raises a `NotFittedError` if the instance is not fitted. This ensures the error is consistent in all estimators with the `get\_feature\_names\_out` method. - :class:`impute.MissingIndicator` - :class:`feature\_extraction.DictVectorizer` - :class:`feature\_extraction.text.TfidfTransformer` - :class:`feature\_selection.GenericUnivariateSelect` - :class:`feature\_selection.RFE` - :class:`feature\_selection.RFECV` - :class:`feature\_selection.SelectFdr` - :class:`feature\_selection.SelectFpr` - :class:`feature\_selection.SelectFromModel` - :class:`feature\_selection.SelectFwe` - :class:`feature\_selection.SelectKBest` - :class:`feature\_selection.SelectPercentile` - :class:`feature\_selection.SequentialFeatureSelector` - :class:`feature\_selection.VarianceThreshold` - :class:`kernel\_approximation.AdditiveChi2Sampler` - :class:`impute.IterativeImputer` - :class:`impute.KNNImputer` - :class:`impute.SimpleImputer` - :class:`isotonic.IsotonicRegression` - :class:`preprocessing.Binarizer` - :class:`preprocessing.KBinsDiscretizer` - :class:`preprocessing.MaxAbsScaler` - :class:`preprocessing.MinMaxScaler` - :class:`preprocessing.Normalizer` - :class:`preprocessing.OrdinalEncoder` - :class:`preprocessing.PowerTransformer` - :class:`preprocessing.QuantileTransformer` - :class:`preprocessing.RobustScaler` - :class:`preprocessing.SplineTransformer` - :class:`preprocessing.StandardScaler` - :class:`random\_projection.GaussianRandomProjection` - :class:`random\_projection.SparseRandomProjection` The `NotFittedError` displays an informative message asking to fit the instance with the appropriate arguments. :pr:`25294`, :pr:`25308`, :pr:`25291`, :pr:`25367`, :pr:`25402`, by :user:`John Pangas `, :user:`Rahil Parikh ` , and :user:`Alex Buzenet `. - \|Enhancement\| Added a multi-threaded Cython routine to the compute squared Euclidean distances (sometimes followed by a fused reduction operation) for a pair of	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.3.rst	main	scikit-learn	[ -0.02754264511168003, -0.09612393379211426, -0.05398266762495041, -0.034078456461429596, 0.03870103508234024, -0.07568904757499695, 0.02858012355864048, 0.0292601827532053, -0.07748554646968842, -0.0006200712523423135, 0.09413136541843414, -0.07869379222393036, 0.03122694045305252, -0.0436...	-0.010809
asking to fit the instance with the appropriate arguments. :pr:`25294`, :pr:`25308`, :pr:`25291`, :pr:`25367`, :pr:`25402`, by :user:`John Pangas `, :user:`Rahil Parikh ` , and :user:`Alex Buzenet `. - \|Enhancement\| Added a multi-threaded Cython routine to the compute squared Euclidean distances (sometimes followed by a fused reduction operation) for a pair of datasets consisting of a sparse CSR matrix and a dense NumPy. This can improve the performance of following functions and estimators: - :func:`sklearn.metrics.pairwise\_distances\_argmin` - :func:`sklearn.metrics.pairwise\_distances\_argmin\_min` - :class:`sklearn.cluster.AffinityPropagation` - :class:`sklearn.cluster.Birch` - :class:`sklearn.cluster.MeanShift` - :class:`sklearn.cluster.OPTICS` - :class:`sklearn.cluster.SpectralClustering` - :func:`sklearn.feature\_selection.mutual\_info\_regression` - :class:`sklearn.neighbors.KNeighborsClassifier` - :class:`sklearn.neighbors.KNeighborsRegressor` - :class:`sklearn.neighbors.RadiusNeighborsClassifier` - :class:`sklearn.neighbors.RadiusNeighborsRegressor` - :class:`sklearn.neighbors.LocalOutlierFactor` - :class:`sklearn.neighbors.NearestNeighbors` - :class:`sklearn.manifold.Isomap` - :class:`sklearn.manifold.LocallyLinearEmbedding` - :class:`sklearn.manifold.TSNE` - :func:`sklearn.manifold.trustworthiness` - :class:`sklearn.semi\_supervised.LabelPropagation` - :class:`sklearn.semi\_supervised.LabelSpreading` A typical example of this performance improvement happens when passing a sparse CSR matrix to the `predict` or `transform` method of estimators that rely on a dense NumPy representation to store their fitted parameters (or the reverse). For instance, :meth:`sklearn.neighbors.NearestNeighbors.kneighbors` is now up to 2 times faster for this case on commonly available laptops. :pr:`25044` by :user:`Julien Jerphanion `. - \|Enhancement\| All estimators that internally rely on OpenMP multi-threading (via Cython) now use a number of threads equal to the number of physical (instead of logical) cores by default. In the past, we observed that using as many threads as logical cores on SMT hosts could sometimes cause severe performance problems depending on the algorithms and the shape of the data. Note that it is still possible to manually adjust the number of threads used by OpenMP as documented in :ref:`parallelism`. :pr:`26082` by :user:`Jérémie du Boisberranger ` and :user:`Olivier Grisel `. Experimental / Under Development -------------------------------- - \|MajorFeature\| :ref:`Metadata routing `'s related base methods are included in this release. This feature is only available via the `enable\_metadata\_routing` feature flag which can be enabled using :func:`sklearn.set\_config` and :func:`sklearn.config\_context`. For now this feature is mostly useful for third party developers to prepare their code base for metadata routing, and we strongly recommend that they also hide it behind the same feature flag, rather than having it enabled by default. :pr:`24027` by `Adrin Jalali`\_, :user:`Benjamin Bossan `, and :user:`Omar Salman `. Changelog --------- .. 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. `sklearn` ......... - \|Feature\| Added a new option `skip\_parameter\_validation`, to the function :func:`sklearn.set\_config` and context manager :func:`sklearn.config\_context`, that allows to skip the validation of the parameters passed to the estimators and public functions. This can be useful to speed up the code but should be used with care because it can lead to unexpected behaviors or raise obscure error messages when setting invalid parameters. :pr:`25815` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.base` ................... - \|Feature\| A `\_\_sklearn\_clone\_\_` protocol is now available to override the default behavior of :func:`base.clone`. :pr:`24568` by `Thomas Fan`\_. - \|Fix\| :class:`base.TransformerMixin` now currently keeps a namedtuple's class if `transform` returns a namedtuple. :pr:`26121` by `Thomas Fan`\_. :mod:`sklearn.calibration` .......................... - \|Fix\| :class:`calibration.CalibratedClassifierCV` now does not enforce sample alignment on `fit\_params`. :pr:`25805` by `Adrin Jalali`\_. :mod:`sklearn.cluster` ...................... - \|MajorFeature\| Added :class:`cluster.HDBSCAN`, a modern hierarchical density-based clustering algorithm. Similarly to :class:`cluster.OPTICS`, it can be seen as a generalization of :class:`cluster.DBSCAN` by allowing for hierarchical instead of flat clustering, however it varies in its approach from :class:`cluster.OPTICS`. This algorithm is very robust with respect to its hyperparameters'	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.3.rst	main	scikit-learn	[ -0.05042300745844841, -0.05853795260190964, -0.07004892826080322, -0.059764351695775986, -0.002899673767387867, -0.053842585533857346, -0.014282973483204842, -0.040785737335681915, -0.02969527430832386, -0.06435644626617432, 0.027769092470407486, -0.09690699726343155, 0.015242253430187702, ...	0.087348
:mod:`sklearn.cluster` ...................... - \|MajorFeature\| Added :class:`cluster.HDBSCAN`, a modern hierarchical density-based clustering algorithm. Similarly to :class:`cluster.OPTICS`, it can be seen as a generalization of :class:`cluster.DBSCAN` by allowing for hierarchical instead of flat clustering, however it varies in its approach from :class:`cluster.OPTICS`. This algorithm is very robust with respect to its hyperparameters' values and can be used on a wide variety of data without much, if any, tuning. This implementation is an adaptation from the original implementation of HDBSCAN in `scikit-learn-contrib/hdbscan `\_, by :user:`Leland McInnes ` et al. :pr:`26385` by :user:`Meekail Zain ` - \|Enhancement\| The `sample\_weight` parameter now will be used in centroids initialization for :class:`cluster.KMeans`, :class:`cluster.BisectingKMeans` and :class:`cluster.MiniBatchKMeans`. This change will break backward compatibility, since numbers generated from same random seeds will be different. :pr:`25752` by :user:`Hleb Levitski `, :user:`Jérémie du Boisberranger `, :user:`Guillaume Lemaitre `. - \|Fix\| :class:`cluster.KMeans`, :class:`cluster.MiniBatchKMeans` and :func:`cluster.k\_means` now correctly handle the combination of `n\_init="auto"` and `init` being an array-like, running one initialization in that case. :pr:`26657` by :user:`Binesh Bannerjee `. - \|API\| The `sample\_weight` parameter in `predict` for :meth:`cluster.KMeans.predict` and :meth:`cluster.MiniBatchKMeans.predict` is now deprecated and will be removed in v1.5. :pr:`25251` by :user:`Hleb Levitski `. - \|API\| The `Xred` argument in :func:`cluster.FeatureAgglomeration.inverse\_transform` is renamed to `Xt` and will be removed in v1.5. :pr:`26503` by `Adrin Jalali`\_. :mod:`sklearn.compose` ...................... - \|Fix\| :class:`compose.ColumnTransformer` raises an informative error when the individual transformers of `ColumnTransformer` output pandas dataframes with indexes that are not consistent with each other and the output is configured to be pandas. :pr:`26286` by `Thomas Fan`\_. - \|Fix\| :class:`compose.ColumnTransformer` correctly sets the output of the remainder when `set\_output` is called. :pr:`26323` by `Thomas Fan`\_. :mod:`sklearn.covariance` ......................... - \|Fix\| Allows `alpha=0` in :class:`covariance.GraphicalLasso` to be consistent with :func:`covariance.graphical\_lasso`. :pr:`26033` by :user:`Genesis Valencia `. - \|Fix\| :func:`covariance.empirical\_covariance` now gives an informative error message when input is not appropriate. :pr:`26108` by :user:`Quentin Barthélemy `. - \|API\| Deprecates `cov\_init` in :func:`covariance.graphical\_lasso` in 1.3 since the parameter has no effect. It will be removed in 1.5. :pr:`26033` by :user:`Genesis Valencia `. - \|API\| Adds `costs\_` fitted attribute in :class:`covariance.GraphicalLasso` and :class:`covariance.GraphicalLassoCV`. :pr:`26033` by :user:`Genesis Valencia `. - \|API\| Adds `covariance` parameter in :class:`covariance.GraphicalLasso`. :pr:`26033` by :user:`Genesis Valencia `. - \|API\| Adds `eps` parameter in :class:`covariance.GraphicalLasso`, :func:`covariance.graphical\_lasso`, and :class:`covariance.GraphicalLassoCV`. :pr:`26033` by :user:`Genesis Valencia `. :mod:`sklearn.datasets` ....................... - \|Enhancement\| Allows to overwrite the parameters used to open the ARFF file using the parameter `read\_csv\_kwargs` in :func:`datasets.fetch\_openml` when using the pandas parser. :pr:`26433` by :user:`Guillaume Lemaitre `. - \|Fix\| :func:`datasets.fetch\_openml` returns improved data types when `as\_frame=True` and `parser="liac-arff"`. :pr:`26386` by `Thomas Fan`\_. - \|Fix\| Following the ARFF specs, only the marker `"?"` is now considered as a missing values when opening ARFF files fetched using :func:`datasets.fetch\_openml` when using the pandas parser. The parameter `read\_csv\_kwargs` allows to overwrite this behaviour. :pr:`26551` by :user:`Guillaume Lemaitre `. - \|Fix\| :func:`datasets.fetch\_openml` will consistently use `np.nan` as missing marker with both parsers `"pandas"` and `"liac-arff"`. :pr:`26579` by :user:`Guillaume Lemaitre `. - \|API\| The `data\_transposed` argument of :func:`datasets.make\_sparse\_coded\_signal` is deprecated and will be removed in v1.5. :pr:`25784` by :user:`Jérémie du Boisberranger`. :mod:`sklearn.decomposition` ............................ - \|Efficiency\| :class:`decomposition.MiniBatchDictionaryLearning` and :class:`decomposition.MiniBatchSparsePCA` are now faster for small batch sizes by avoiding duplicate validations. :pr:`25490` by :user:`Jérémie du Boisberranger `. - \|Enhancement\| :class:`decomposition.DictionaryLearning` now accepts the parameter `callback` for consistency with the function :func:`decomposition.dict\_learning`. :pr:`24871` by :user:`Omar Salman `. - \|Fix\| Treat more consistently small values in the `W` and `H` matrices during the `fit` and `transform` steps of :class:`decomposition.NMF` and :class:`decomposition.MiniBatchNMF` which can produce different results than previous versions. :pr:`25438` by :user:`Yotam Avidar-Constantini `. - \|API\| The `W` argument in :func:`decomposition.NMF.inverse\_transform` and :class:`decomposition.MiniBatchNMF.inverse\_transform` is renamed to `Xt` and will be removed in v1.5. :pr:`26503`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.3.rst	main	scikit-learn	[ -0.0333096981048584, -0.04358741268515587, -0.046735022217035294, -0.0011739972978830338, 0.05324135348200798, -0.06677720695734024, 0.02284185402095318, -0.056183572858572006, -0.07270092517137527, 0.020289378240704536, -0.03654592111706734, -0.013994678854942322, 0.08730757981538773, -0....	0.13987
values in the `W` and `H` matrices during the `fit` and `transform` steps of :class:`decomposition.NMF` and :class:`decomposition.MiniBatchNMF` which can produce different results than previous versions. :pr:`25438` by :user:`Yotam Avidar-Constantini `. - \|API\| The `W` argument in :func:`decomposition.NMF.inverse\_transform` and :class:`decomposition.MiniBatchNMF.inverse\_transform` is renamed to `Xt` and will be removed in v1.5. :pr:`26503` by `Adrin Jalali`\_. :mod:`sklearn.discriminant\_analysis` .................................... - \|Enhancement\| :class:`discriminant\_analysis.LinearDiscriminantAnalysis` now supports the `PyTorch `\_\_. See :ref:`array\_api` for more details. :pr:`25956` by `Thomas Fan`\_. :mod:`sklearn.ensemble` ....................... - \|Feature\| :class:`ensemble.HistGradientBoostingRegressor` now supports the Gamma deviance loss via `loss="gamma"`. Using the Gamma deviance as loss function comes in handy for modelling skewed distributed, strictly positive valued targets. :pr:`22409` by :user:`Christian Lorentzen `. - \|Feature\| Compute a custom out-of-bag score by passing a callable to :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier` and :class:`ensemble.ExtraTreesRegressor`. :pr:`25177` by `Tim Head`\_. - \|Feature\| :class:`ensemble.GradientBoostingClassifier` now exposes out-of-bag scores via the `oob\_scores\_` or `oob\_score\_` attributes. :pr:`24882` by :user:`Ashwin Mathur `. - \|Efficiency\| :class:`ensemble.IsolationForest` predict time is now faster (typically by a factor of 8 or more). Internally, the estimator now precomputes decision path lengths per tree at `fit` time. It is therefore not possible to load an estimator trained with scikit-learn 1.2 to make it predict with scikit-learn 1.3: retraining with scikit-learn 1.3 is required. :pr:`25186` by :user:`Felipe Breve Siola `. - \|Efficiency\| :class:`ensemble.RandomForestClassifier` and :class:`ensemble.RandomForestRegressor` with `warm\_start=True` now only recomputes out-of-bag scores when there are actually more `n\_estimators` in subsequent `fit` calls. :pr:`26318` by :user:`Joshua Choo Yun Keat `. - \|Enhancement\| :class:`ensemble.BaggingClassifier` and :class:`ensemble.BaggingRegressor` expose the `allow\_nan` tag from the underlying estimator. :pr:`25506` by `Thomas Fan`\_. - \|Fix\| :meth:`ensemble.RandomForestClassifier.fit` sets `max\_samples = 1` when `max\_samples` is a float and `round(n\_samples \* max\_samples) < 1`. :pr:`25601` by :user:`Jan Fidor `. - \|Fix\| :meth:`ensemble.IsolationForest.fit` no longer warns about missing feature names when called with `contamination` not `"auto"` on a pandas dataframe. :pr:`25931` by :user:`Yao Xiao `. - \|Fix\| :class:`ensemble.HistGradientBoostingRegressor` and :class:`ensemble.HistGradientBoostingClassifier` treats negative values for categorical features consistently as missing values, following LightGBM's and pandas' conventions. :pr:`25629` by `Thomas Fan`\_. - \|Fix\| Fix deprecation of `base\_estimator` in :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` that was introduced in :pr:`23819`. :pr:`26242` by :user:`Marko Toplak `. :mod:`sklearn.exceptions` ......................... - \|Feature\| Added :class:`exceptions.InconsistentVersionWarning` which is raised when a scikit-learn estimator is unpickled with a scikit-learn version that is inconsistent with the scikit-learn version the estimator was pickled with. :pr:`25297` by `Thomas Fan`\_. :mod:`sklearn.feature\_extraction` ................................. - \|API\| :class:`feature\_extraction.image.PatchExtractor` now follows the transformer API of scikit-learn. This class is defined as a stateless transformer meaning that it is not required to call `fit` before calling `transform`. Parameter validation only happens at `fit` time. :pr:`24230` by :user:`Guillaume Lemaitre `. :mod:`sklearn.feature\_selection` ................................ - \|Enhancement\| All selectors in :mod:`sklearn.feature\_selection` will preserve a DataFrame's dtype when transformed. :pr:`25102` by `Thomas Fan`\_. - \|Fix\| :class:`feature\_selection.SequentialFeatureSelector`'s `cv` parameter now supports generators. :pr:`25973` by `Yao Xiao `. :mod:`sklearn.impute` ..................... - \|Enhancement\| Added the parameter `fill\_value` to :class:`impute.IterativeImputer`. :pr:`25232` by :user:`Thijs van Weezel `. - \|Fix\| :class:`impute.IterativeImputer` now correctly preserves the Pandas Index when the `set\_config(transform\_output="pandas")`. :pr:`26454` by `Thomas Fan`\_. :mod:`sklearn.inspection` ......................... - \|Enhancement\| Added support for `sample\_weight` in :func:`inspection.partial\_dependence` and :meth:`inspection.PartialDependenceDisplay.from\_estimator`. This allows for weighted averaging when aggregating for each value of the grid we are making the inspection on. The option is only available when `method` is set to `brute`. :pr:`25209` and :pr:`26644` by :user:`Carlo Lemos `. - \|API\| :func:`inspection.partial\_dependence` returns a :class:`utils.Bunch` with new key: `grid\_values`. The `values` key is deprecated in favor of `grid\_values` and the `values` key will be removed in 1.5. :pr:`21809` and :pr:`25732` by `Thomas Fan`\_. :mod:`sklearn.kernel\_approximation` ................................... - \|Fix\| :class:`kernel\_approximation.AdditiveChi2Sampler` is now stateless. The `sample\_interval\_` attribute is deprecated and will be removed in 1.5. :pr:`25190` by :user:`Vincent Maladière `. :mod:`sklearn.linear\_model` ........................... - \|Efficiency\|	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.3.rst	main	scikit-learn	[ -0.14059387147426605, -0.016168899834156036, -0.12579035758972168, -0.03010782226920128, 0.03007682040333748, -0.05614259093999863, -0.019371168687939644, 0.03298186883330345, -0.0696062445640564, -0.015502630732953548, 0.09835583716630936, 0.02381519228219986, -0.05329480767250061, -0.017...	0.094836
key is deprecated in favor of `grid\_values` and the `values` key will be removed in 1.5. :pr:`21809` and :pr:`25732` by `Thomas Fan`\_. :mod:`sklearn.kernel\_approximation` ................................... - \|Fix\| :class:`kernel\_approximation.AdditiveChi2Sampler` is now stateless. The `sample\_interval\_` attribute is deprecated and will be removed in 1.5. :pr:`25190` by :user:`Vincent Maladière `. :mod:`sklearn.linear\_model` ........................... - \|Efficiency\| Avoid data scaling when `sample\_weight=None` and other unnecessary data copies and unexpected dense to sparse data conversion in :class:`linear\_model.LinearRegression`. :pr:`26207` by :user:`Olivier Grisel `. - \|Enhancement\| :class:`linear\_model.SGDClassifier`, :class:`linear\_model.SGDRegressor` and :class:`linear\_model.SGDOneClassSVM` now preserve dtype for `numpy.float32`. :pr:`25587` by :user:`Omar Salman `. - \|Enhancement\| The `n\_iter\_` attribute has been included in :class:`linear\_model.ARDRegression` to expose the actual number of iterations required to reach the stopping criterion. :pr:`25697` by :user:`John Pangas `. - \|Fix\| Use a more robust criterion to detect convergence of :class:`linear\_model.LogisticRegression` with `penalty="l1"` and `solver="liblinear"` on linearly separable problems. :pr:`25214` by `Tom Dupre la Tour`\_. - \|Fix\| Fix a crash when calling `fit` on :class:`linear\_model.LogisticRegression` with `solver="newton-cholesky"` and `max\_iter=0` which failed to inspect the state of the model prior to the first parameter update. :pr:`26653` by :user:`Olivier Grisel `. - \|API\| Deprecates `n\_iter` in favor of `max\_iter` in :class:`linear\_model.BayesianRidge` and :class:`linear\_model.ARDRegression`. `n\_iter` will be removed in scikit-learn 1.5. This change makes those estimators consistent with the rest of estimators. :pr:`25697` by :user:`John Pangas `. :mod:`sklearn.manifold` ....................... - \|Fix\| :class:`manifold.Isomap` now correctly preserves the Pandas Index when the `set\_config(transform\_output="pandas")`. :pr:`26454` by `Thomas Fan`\_. :mod:`sklearn.metrics` ...................... - \|Feature\| Adds `zero\_division=np.nan` to multiple classification metrics: :func:`metrics.precision\_score`, :func:`metrics.recall\_score`, :func:`metrics.f1\_score`, :func:`metrics.fbeta\_score`, :func:`metrics.precision\_recall\_fscore\_support`, :func:`metrics.classification\_report`. When `zero\_division=np.nan` and there is a zero division, the metric is undefined and is excluded from averaging. When not used for averages, the value returned is `np.nan`. :pr:`25531` by :user:`Marc Torrellas Socastro `. - \|Feature\| :func:`metrics.average\_precision\_score` now supports the multiclass case. :pr:`17388` by :user:`Geoffrey Bolmier ` and :pr:`24769` by :user:`Ashwin Mathur `. - \|Efficiency\| The computation of the expected mutual information in :func:`metrics.adjusted\_mutual\_info\_score` is now faster when the number of unique labels is large and its memory usage is reduced in general. :pr:`25713` by :user:`Kshitij Mathur `, :user:`Guillaume Lemaitre `, :user:`Omar Salman ` and :user:`Jérémie du Boisberranger `. - \|Enhancement\| :class:`metrics.silhouette\_samples` now accepts a sparse matrix of pairwise distances between samples, or a feature array. :pr:`18723` by :user:`Sahil Gupta ` and :pr:`24677` by :user:`Ashwin Mathur `. - \|Enhancement\| A new parameter `drop\_intermediate` was added to :func:`metrics.precision\_recall\_curve`, :func:`metrics.PrecisionRecallDisplay.from\_estimator`, :func:`metrics.PrecisionRecallDisplay.from\_predictions`, which drops some suboptimal thresholds to create lighter precision-recall curves. :pr:`24668` by :user:`dberenbaum`. - \|Enhancement\| :meth:`metrics.RocCurveDisplay.from\_estimator` and :meth:`metrics.RocCurveDisplay.from\_predictions` now accept two new keywords, `plot\_chance\_level` and `chance\_level\_kw` to plot the baseline chance level. This line is exposed in the `chance\_level\_` attribute. :pr:`25987` by :user:`Yao Xiao `. - \|Enhancement\| :meth:`metrics.PrecisionRecallDisplay.from\_estimator` and :meth:`metrics.PrecisionRecallDisplay.from\_predictions` now accept two new keywords, `plot\_chance\_level` and `chance\_level\_kw` to plot the baseline chance level. This line is exposed in the `chance\_level\_` attribute. :pr:`26019` by :user:`Yao Xiao `. - \|Fix\| :func:`metrics.pairwise.manhattan\_distances` now supports readonly sparse datasets. :pr:`25432` by :user:`Julien Jerphanion `. - \|Fix\| Fixed :func:`metrics.classification\_report` so that empty input will return `np.nan`. Previously, "macro avg" and `weighted avg` would return e.g. `f1-score=np.nan` and `f1-score=0.0`, being inconsistent. Now, they both return `np.nan`. :pr:`25531` by :user:`Marc Torrellas Socastro `. - \|Fix\| :func:`metrics.ndcg\_score` now gives a meaningful error message for input of length 1. :pr:`25672` by :user:`Lene Preuss ` and :user:`Wei-Chun Chu `. - \|Fix\| :func:`metrics.log\_loss` raises a warning if the values of the parameter `y\_pred` are not normalized, instead of actually normalizing them in the metric. Starting from 1.5 this will raise an error. :pr:`25299` by :user:`Omar Salman `. - \|Fix\| The `'matching'` metric has been removed when using SciPy>=1.9 to be consistent with `scipy.spatial.distance` which does not support `'matching'` anymore. :pr:`26264` by :user:`Barata T. Onggo ` -	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.3.rst	main	scikit-learn	[ -0.05895417556166649, -0.0017705821665003896, -0.0665188655257225, 0.007326913997530937, 0.06063123792409897, -0.07938346266746521, 0.018889375030994415, 0.01458179671317339, -0.012859056703746319, 0.010412933304905891, 0.12010657787322998, -0.00449170358479023, 0.04900965467095375, -0.122...	0.051511
instead of actually normalizing them in the metric. Starting from 1.5 this will raise an error. :pr:`25299` by :user:`Omar Salman `. - \|Fix\| The `'matching'` metric has been removed when using SciPy>=1.9 to be consistent with `scipy.spatial.distance` which does not support `'matching'` anymore. :pr:`26264` by :user:`Barata T. Onggo ` - \|API\| The `eps` parameter of the :func:`metrics.log\_loss` has been deprecated and will be removed in 1.5. :pr:`25299` by :user:`Omar Salman `. :mod:`sklearn.gaussian\_process` ............................... - \|Fix\| :class:`gaussian\_process.GaussianProcessRegressor` has a new argument `n\_targets`, which is used to decide the number of outputs when sampling from the prior distributions. :pr:`23099` by :user:`Zhehao Liu `. :mod:`sklearn.mixture` ...................... - \|Efficiency\| :class:`mixture.GaussianMixture` is more efficient now and will bypass unnecessary initialization if the weights, means, and precisions are given by users. :pr:`26021` by :user:`Jiawei Zhang `. :mod:`sklearn.model\_selection` .............................. - \|MajorFeature\| Added the class :class:`model\_selection.ValidationCurveDisplay` that allows easy plotting of validation curves obtained by the function :func:`model\_selection.validation\_curve`. :pr:`25120` by :user:`Guillaume Lemaitre `. - \|API\| The parameter `log\_scale` in the method `plot` of the class :class:`model\_selection.LearningCurveDisplay` has been deprecated in 1.3 and will be removed in 1.5. The default scale can be overridden by setting it directly on the `ax` object and will be set automatically from the spacing of the data points otherwise. :pr:`25120` by :user:`Guillaume Lemaitre `. - \|Enhancement\| :func:`model\_selection.cross\_validate` accepts a new parameter `return\_indices` to return the train-test indices of each cv split. :pr:`25659` by :user:`Guillaume Lemaitre `. :mod:`sklearn.multioutput` .......................... - \|Fix\| :func:`getattr` on :meth:`multioutput.MultiOutputRegressor.partial\_fit` and :meth:`multioutput.MultiOutputClassifier.partial\_fit` now correctly raise an `AttributeError` if done before calling `fit`. :pr:`26333` by `Adrin Jalali`\_. :mod:`sklearn.naive\_bayes` .......................... - \|Fix\| :class:`naive\_bayes.GaussianNB` does not raise anymore a `ZeroDivisionError` when the provided `sample\_weight` reduces the problem to a single class in `fit`. :pr:`24140` by :user:`Jonathan Ohayon ` and :user:`Chiara Marmo `. :mod:`sklearn.neighbors` ........................ - \|Enhancement\| The performance of :meth:`neighbors.KNeighborsClassifier.predict` and of :meth:`neighbors.KNeighborsClassifier.predict\_proba` has been improved when `n\_neighbors` is large and `algorithm="brute"` with non Euclidean metrics. :pr:`24076` by :user:`Meekail Zain `, :user:`Julien Jerphanion `. - \|Fix\| Remove support for `KulsinskiDistance` in :class:`neighbors.BallTree`. This dissimilarity is not a metric and cannot be supported by the BallTree. :pr:`25417` by :user:`Guillaume Lemaitre `. - \|API\| The support for metrics other than `euclidean` and `manhattan` and for callables in :class:`neighbors.NearestNeighbors` is deprecated and will be removed in version 1.5. :pr:`24083` by :user:`Valentin Laurent `. :mod:`sklearn.neural\_network` ............................. - \|Fix\| :class:`neural\_network.MLPRegressor` and :class:`neural\_network.MLPClassifier` reports the right `n\_iter\_` when `warm\_start=True`. It corresponds to the number of iterations performed on the current call to `fit` instead of the total number of iterations performed since the initialization of the estimator. :pr:`25443` by :user:`Marvin Krawutschke `. :mod:`sklearn.pipeline` ....................... - \|Feature\| :class:`pipeline.FeatureUnion` can now use indexing notation (e.g. `feature\_union["scalar"]`) to access transformers by name. :pr:`25093` by `Thomas Fan`\_. - \|Feature\| :class:`pipeline.FeatureUnion` can now access the `feature\_names\_in\_` attribute if the `X` value seen during `.fit` has a `columns` attribute and all columns are strings. e.g. when `X` is a `pandas.DataFrame` :pr:`25220` by :user:`Ian Thompson `. - \|Fix\| :meth:`pipeline.Pipeline.fit\_transform` now raises an `AttributeError` if the last step of the pipeline does not support `fit\_transform`. :pr:`26325` by `Adrin Jalali`\_. :mod:`sklearn.preprocessing` ............................ - \|MajorFeature\| Introduces :class:`preprocessing.TargetEncoder` which is a categorical encoding based on target mean conditioned on the value of the category. :pr:`25334` by `Thomas Fan`\_. - \|Feature\| :class:`preprocessing.OrdinalEncoder` now supports grouping infrequent categories into a single feature. Grouping infrequent categories is enabled by specifying how to select infrequent categories with `min\_frequency` or `max\_categories`. :pr:`25677` by `Thomas Fan`\_. - \|Enhancement\| :class:`preprocessing.PolynomialFeatures` now calculates the number of expanded terms a-priori when dealing with sparse `csr` matrices in order to optimize the choice of `dtype` for `indices` and `indptr`. It can now output `csr` matrices with `np.int32` `indices/indptr` components when	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.3.rst	main	scikit-learn	[ -0.06022784486413002, -0.06615357100963593, -0.01322624459862709, -0.006373412907123566, 0.010105141438543797, -0.053872063755989075, 0.05550619959831238, 0.012816932052373886, -0.038268525153398514, -0.014043605886399746, 0.0019665518775582314, -0.0053070648573338985, 0.04739253595471382, ...	0.122139
infrequent categories with `min\_frequency` or `max\_categories`. :pr:`25677` by `Thomas Fan`\_. - \|Enhancement\| :class:`preprocessing.PolynomialFeatures` now calculates the number of expanded terms a-priori when dealing with sparse `csr` matrices in order to optimize the choice of `dtype` for `indices` and `indptr`. It can now output `csr` matrices with `np.int32` `indices/indptr` components when there are few enough elements, and will automatically use `np.int64` for sufficiently large matrices. :pr:`20524` by :user:`niuk-a ` and :pr:`23731` by :user:`Meekail Zain ` - \|Enhancement\| A new parameter `sparse\_output` was added to :class:`preprocessing.SplineTransformer`, available as of SciPy 1.8. If `sparse\_output=True`, :class:`preprocessing.SplineTransformer` returns a sparse CSR matrix. :pr:`24145` by :user:`Christian Lorentzen `. - \|Enhancement\| Adds a `feature\_name\_combiner` parameter to :class:`preprocessing.OneHotEncoder`. This specifies a custom callable to create feature names to be returned by :meth:`preprocessing.OneHotEncoder.get\_feature\_names\_out`. The callable combines input arguments `(input\_feature, category)` to a string. :pr:`22506` by :user:`Mario Kostelac `. - \|Enhancement\| Added support for `sample\_weight` in :class:`preprocessing.KBinsDiscretizer`. This allows specifying the parameter `sample\_weight` for each sample to be used while fitting. The option is only available when `strategy` is set to `quantile` and `kmeans`. :pr:`24935` by :user:`Seladus `, :user:`Guillaume Lemaitre `, and :user:`Dea María Léon `, :pr:`25257` by :user:`Hleb Levitski `. - \|Enhancement\| Subsampling through the `subsample` parameter can now be used in :class:`preprocessing.KBinsDiscretizer` regardless of the strategy used. :pr:`26424` by :user:`Jérémie du Boisberranger `. - \|Fix\| :class:`preprocessing.PowerTransformer` now correctly preserves the Pandas Index when the `set\_config(transform\_output="pandas")`. :pr:`26454` by `Thomas Fan`\_. - \|Fix\| :class:`preprocessing.PowerTransformer` now correctly raises error when using `method="box-cox"` on data with a constant `np.nan` column. :pr:`26400` by :user:`Yao Xiao `. - \|Fix\| :class:`preprocessing.PowerTransformer` with `method="yeo-johnson"` now leaves constant features unchanged instead of transforming with an arbitrary value for the `lambdas\_` fitted parameter. :pr:`26566` by :user:`Jérémie du Boisberranger `. - \|API\| The default value of the `subsample` parameter of :class:`preprocessing.KBinsDiscretizer` will change from `None` to `200\_000` in version 1.5 when `strategy="kmeans"` or `strategy="uniform"`. :pr:`26424` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.svm` .................. - \|API\| `dual` parameter now accepts `auto` option for :class:`svm.LinearSVC` and :class:`svm.LinearSVR`. :pr:`26093` by :user:`Hleb Levitski `. :mod:`sklearn.tree` ................... - \|MajorFeature\| :class:`tree.DecisionTreeRegressor` and :class:`tree.DecisionTreeClassifier` support missing values when `splitter='best'` and criterion is `gini`, `entropy`, or `log\_loss`, for classification or `squared\_error`, `friedman\_mse`, or `poisson` for regression. :pr:`23595`, :pr:`26376` by `Thomas Fan`\_. - \|Enhancement\| Adds a `class\_names` parameter to :func:`tree.export\_text`. This allows specifying the parameter `class\_names` for each target class in ascending numerical order. :pr:`25387` by :user:`William M ` and :user:`crispinlogan `. - \|Fix\| :func:`tree.export\_graphviz` and :func:`tree.export\_text` now accepts `feature\_names` and `class\_names` as array-like rather than lists. :pr:`26289` by :user:`Yao Xiao ` :mod:`sklearn.utils` .................... - \|FIX\| Fixes :func:`utils.check\_array` to properly convert pandas extension arrays. :pr:`25813` and :pr:`26106` by `Thomas Fan`\_. - \|Fix\| :func:`utils.check\_array` now supports pandas DataFrames with extension arrays and object dtypes by returning an ndarray with object dtype. :pr:`25814` by `Thomas Fan`\_. - \|API\| `utils.estimator\_checks.check\_transformers\_unfitted\_stateless` has been introduced to ensure stateless transformers don't raise `NotFittedError` during `transform` with no prior call to `fit` or `fit\_transform`. :pr:`25190` by :user:`Vincent Maladière `. - \|API\| A `FutureWarning` is now raised when instantiating a class which inherits from a deprecated base class (i.e. decorated by :class:`utils.deprecated`) and which overrides the `\_\_init\_\_` method. :pr:`25733` by :user:`Brigitta Sipőcz ` and :user:`Jérémie du Boisberranger `. :mod:`sklearn.semi\_supervised` .............................. - \|Enhancement\| :meth:`semi\_supervised.LabelSpreading.fit` and :meth:`semi\_supervised.LabelPropagation.fit` now accepts sparse metrics. :pr:`19664` by :user:`Kaushik Amar Das `. Miscellaneous ............. - \|Enhancement\| Replace obsolete exceptions `EnvironmentError`, `IOError` and `WindowsError`. :pr:`26466` by :user:`Dimitri Papadopoulos ORfanos `. .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.2, including: 2357juan, Abhishek Singh Kushwah, Adam Handke, Adam Kania, Adam Li, adienes, Admir Demiraj, adoublet, Adrin Jalali, A.H.Mansouri, Ahmedbgh, Ala-Na, Alex Buzenet,	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.3.rst	main	scikit-learn	[ -0.026201147586107254, -0.012972352094948292, -0.11966405808925629, 0.04456857219338417, -0.014008679427206516, -0.08710529655218124, -0.07518263906240463, -0.03338988870382309, -0.05476164445281029, 0.013899321667850018, 0.050123173743486404, -0.03815698251128197, -0.011157972738146782, -...	0.017794
by :user:`Dimitri Papadopoulos ORfanos `. .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.2, including: 2357juan, Abhishek Singh Kushwah, Adam Handke, Adam Kania, Adam Li, adienes, Admir Demiraj, adoublet, Adrin Jalali, A.H.Mansouri, Ahmedbgh, Ala-Na, Alex Buzenet, AlexL, Ali H. El-Kassas, amay, András Simon, André Pedersen, Andrew Wang, Ankur Singh, annegnx, Ansam Zedan, Anthony22-dev, Artur Hermano, Arturo Amor, as-90, ashah002, Ashish Dutt, Ashwin Mathur, AymericBasset, Azaria Gebremichael, Barata Tripramudya Onggo, Benedek Harsanyi, Benjamin Bossan, Bharat Raghunathan, Binesh Bannerjee, Boris Feld, Brendan Lu, Brevin Kunde, cache-missing, Camille Troillard, Carla J, carlo, Carlo Lemos, c-git, Changyao Chen, Chiara Marmo, Christian Lorentzen, Christian Veenhuis, Christine P. Chai, crispinlogan, Da-Lan, DanGonite57, Dave Berenbaum, davidblnc, david-cortes, Dayne, Dea María Léon, Denis, Dimitri Papadopoulos Orfanos, Dimitris Litsidis, Dmitry Nesterov, Dominic Fox, Dominik Prodinger, Edern, Ekaterina Butyugina, Elabonga Atuo, Emir, farhan khan, Felipe Siola, futurewarning, Gael Varoquaux, genvalen, Hleb Levitski, Guillaume Lemaitre, gunesbayir, Haesun Park, hujiahong726, i-aki-y, Ian Thompson, Ido M, Ily, Irene, Jack McIvor, jakirkham, James Dean, JanFidor, Jarrod Millman, JB Mountford, Jérémie du Boisberranger, Jessicakk0711, Jiawei Zhang, Joey Ortiz, JohnathanPi, John Pangas, Joshua Choo Yun Keat, Joshua Hedlund, JuliaSchoepp, Julien Jerphanion, jygerardy, ka00ri, Kaushik Amar Das, Kento Nozawa, Kian Eliasi, Kilian Kluge, Lene Preuss, Linus, Logan Thomas, Loic Esteve, Louis Fouquet, Lucy Liu, Madhura Jayaratne, Marc Torrellas Socastro, Maren Westermann, Mario Kostelac, Mark Harfouche, Marko Toplak, Marvin Krawutschke, Masanori Kanazu, mathurinm, Matt Haberland, Max Halford, maximeSaur, Maxwell Liu, m. bou, mdarii, Meekail Zain, Mikhail Iljin, murezzda, Nawazish Alam, Nicola Fanelli, Nightwalkx, Nikolay Petrov, Nishu Choudhary, NNLNR, npache, Olivier Grisel, Omar Salman, ouss1508, PAB, Pandata, partev, Peter Piontek, Phil, pnucci, Pooja M, Pooja Subramaniam, precondition, Quentin Barthélemy, Rafal Wojdyla, Raghuveer Bhat, Rahil Parikh, Ralf Gommers, ram vikram singh, Rushil Desai, Sadra Barikbin, SANJAI\_3, Sashka Warner, Scott Gigante, Scott Gustafson, searchforpassion, Seoeun Hong, Shady el Gewily, Shiva chauhan, Shogo Hida, Shreesha Kumar Bhat, sonnivs, Sortofamudkip, Stanislav (Stanley) Modrak, Stefanie Senger, Steven Van Vaerenbergh, Tabea Kossen, Théophile Baranger, Thijs van Weezel, Thomas A Caswell, Thomas Germer, Thomas J. Fan, Tim Head, Tim P, Tom Dupré la Tour, tomiock, tspeng, Valentin Laurent, Veghit, VIGNESH D, Vijeth Moudgalya, Vinayak Mehta, Vincent M, Vincent-violet, Vyom Pathak, William M, windiana42, Xiao Yuan, Yao Xiao, Yaroslav Halchenko, Yotam Avidar-Constantini, Yuchen Zhou, Yusuf Raji, zeeshan lone	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.3.rst	main	scikit-learn	[ -0.0752064436674118, -0.0444432832300663, -0.03490854427218437, -0.03688668832182884, -0.025884859263896942, -0.03795115277171135, -0.05423586070537567, 0.043343205004930496, -0.012039068154990673, 0.027448568493127823, -0.007749597541987896, 0.055290549993515015, 0.03000972792506218, -0.0...	0.196408
.. include:: \_contributors.rst .. currentmodule:: sklearn ============ Version 0.20 ============ .. warning:: Version 0.20 is the last version of scikit-learn to support Python 2.7 and Python 3.4. Scikit-learn 0.21 will require Python 3.5 or higher. .. include:: changelog\_legend.inc .. \_changes\_0\_20\_4: Version 0.20.4 ============== \\July 30, 2019\\ This is a bug-fix release with some bug fixes applied to version 0.20.3. Changelog --------- The bundled version of joblib was upgraded from 0.13.0 to 0.13.2. :mod:`sklearn.cluster` .............................. - \|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.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.model\_selection` .............................. - \|Fix\| Fixed a bug where :class:`model\_selection.StratifiedKFold` shuffles each class's samples with the same ``random\_state``, making ``shuffle=True`` ineffective. :issue:`13124` by :user:`Hanmin Qin `. :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\_20\_3: Version 0.20.3 ============== \\March 1, 2019\\ This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0. Changelog --------- :mod:`sklearn.cluster` ...................... - \|Fix\| Fixed a bug in :class:`cluster.KMeans` where computation was single threaded when `n\_jobs > 1` or `n\_jobs = -1`. :issue:`12949` by :user:`Prabakaran Kumaresshan `. :mod:`sklearn.compose` ...................... - \|Fix\| Fixed a bug in :class:`compose.ColumnTransformer` to handle negative indexes in the columns list of the transformers. :issue:`12946` by :user:`Pierre Tallotte `. :mod:`sklearn.covariance` ......................... - \|Fix\| Fixed a regression in :func:`covariance.graphical\_lasso` so that the case `n\_features=2` is handled correctly. :issue:`13276` by :user:`Aurélien Bellet `. :mod:`sklearn.decomposition` ............................ - \|Fix\| Fixed a bug in :func:`decomposition.sparse\_encode` where computation was single threaded when `n\_jobs > 1` or `n\_jobs = -1`. :issue:`13005` by :user:`Prabakaran Kumaresshan `. :mod:`sklearn.datasets` ............................ - \|Efficiency\| :func:`sklearn.datasets.fetch\_openml` now loads data by streaming, avoiding high memory usage. :issue:`13312` by `Joris Van den Bossche`\_. :mod:`sklearn.feature\_extraction` ................................. - \|Fix\| Fixed a bug in :class:`feature\_extraction.text.CountVectorizer` which would result in the sparse feature matrix having conflicting `indptr` and `indices` precisions under very large vocabularies. :issue:`11295` by :user:`Gabriel Vacaliuc `. :mod:`sklearn.impute` ..................... - \|Fix\| add support for non-numeric data in :class:`sklearn.impute.MissingIndicator` which was not supported while :class:`sklearn.impute.SimpleImputer` was supporting this for some imputation strategies. :issue:`13046` by :user:`Guillaume Lemaitre `. :mod:`sklearn.linear\_model` ........................... - \|Fix\| Fixed a bug in :class:`linear\_model.MultiTaskElasticNet` and :class:`linear\_model.MultiTaskLasso` which were breaking when ``warm\_start = True``. :issue:`12360` by :user:`Aakanksha Joshi `. :mod:`sklearn.preprocessing` ............................ - \|Fix\| Fixed a bug in :class:`preprocessing.KBinsDiscretizer` where ``strategy='kmeans'`` fails with an error during transformation due to unsorted bin edges. :issue:`13134` by :user:`Sandro Casagrande `. - \|Fix\| Fixed a bug in :class:`preprocessing.OneHotEncoder` where the deprecation of ``categorical\_features`` was handled incorrectly in combination with ``handle\_unknown='ignore'``. :issue:`12881` by `Joris Van den Bossche`\_. - \|Fix\| Bins whose width are too small (i.e., <= 1e-8) are removed with a warning in :class:`preprocessing.KBinsDiscretizer`. :issue:`13165` by :user:`Hanmin Qin `. :mod:`sklearn.svm` .................. - \|FIX\| Fixed a bug in :class:`svm.SVC`, :class:`svm.NuSVC`, :class:`svm.SVR`, :class:`svm.NuSVR` and :class:`svm.OneClassSVM` where the ``scale`` option of parameter ``gamma`` is erroneously defined as ``1 / (n\_features \* X.std())``. It's now defined as ``1 / (n\_features \* X.var())``. :issue:`13221` by :user:`Hanmin Qin `. Code and Documentation Contributors ----------------------------------- With thanks to: Adrin Jalali, Agamemnon Krasoulis, Albert Thomas, Andreas Mueller, Aurélien Bellet, bertrandhaut, Bharat Raghunathan, Dowon, Emmanuel Arias, Fibinse Xavier, Finn O'Shea, Gabriel Vacaliuc, Gael Varoquaux, Guillaume Lemaitre, Hanmin Qin, joaak, Joel Nothman, Joris Van den Bossche, Jérémie Méhault,	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.05099266767501831, -0.031204134225845337, -0.011138316243886948, 0.027451016008853912, 0.1712731420993805, -0.0566394180059433, -0.05155438929796219, -0.022521572187542915, -0.0471440814435482, 0.023627804592251778, 0.02324865013360977, 0.0024847849272191525, -0.03919524699449539, 0.019...	0.08665
:issue:`13221` by :user:`Hanmin Qin `. Code and Documentation Contributors ----------------------------------- With thanks to: Adrin Jalali, Agamemnon Krasoulis, Albert Thomas, Andreas Mueller, Aurélien Bellet, bertrandhaut, Bharat Raghunathan, Dowon, Emmanuel Arias, Fibinse Xavier, Finn O'Shea, Gabriel Vacaliuc, Gael Varoquaux, Guillaume Lemaitre, Hanmin Qin, joaak, Joel Nothman, Joris Van den Bossche, Jérémie Méhault, kms15, Kossori Aruku, Lakshya KD, maikia, Manuel López-Ibáñez, Marco Gorelli, MarcoGorelli, mferrari3, Mickaël Schoentgen, Nicolas Hug, pavlos kallis, Pierre Glaser, pierretallotte, Prabakaran Kumaresshan, Reshama Shaikh, Rohit Kapoor, Roman Yurchak, SandroCasagrande, Tashay Green, Thomas Fan, Vishaal Kapoor, Zhuyi Xue, Zijie (ZJ) Poh .. \_changes\_0\_20\_2: Version 0.20.2 ============== \\December 20, 2018\\ This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0. 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. - :mod:`sklearn.neighbors` when ``metric=='jaccard'`` (bug fix) - use of ``'seuclidean'`` or ``'mahalanobis'`` metrics in some cases (bug fix) Changelog --------- :mod:`sklearn.compose` ...................... - \|Fix\| Fixed an issue in :func:`compose.make\_column\_transformer` which raises unexpected error when columns is pandas Index or pandas Series. :issue:`12704` by :user:`Hanmin Qin `. :mod:`sklearn.metrics` ...................... - \|Fix\| Fixed a bug in :func:`metrics.pairwise\_distances` and :func:`metrics.pairwise\_distances\_chunked` where parameters ``V`` of ``"seuclidean"`` and ``VI`` of ``"mahalanobis"`` metrics were computed after the data was split into chunks instead of being pre-computed on whole data. :issue:`12701` by :user:`Jeremie du Boisberranger `. :mod:`sklearn.neighbors` ........................ - \|Fix\| Fixed `sklearn.neighbors.DistanceMetric` jaccard distance function to return 0 when two all-zero vectors are compared. :issue:`12685` by :user:`Thomas Fan `. :mod:`sklearn.utils` .................... - \|Fix\| Calling :func:`utils.check\_array` on `pandas.Series` with categorical data, which raised an error in 0.20.0, now returns the expected output again. :issue:`12699` by `Joris Van den Bossche`\_. Code and Documentation Contributors ----------------------------------- With thanks to: adanhawth, Adrin Jalali, Albert Thomas, Andreas Mueller, Dan Stine, Feda Curic, Hanmin Qin, Jan S, jeremiedbb, Joel Nothman, Joris Van den Bossche, josephsalmon, Katrin Leinweber, Loic Esteve, Muhammad Hassaan Rafique, Nicolas Hug, Olivier Grisel, Paul Paczuski, Reshama Shaikh, Sam Waterbury, Shivam Kotwalia, Thomas Fan .. \_changes\_0\_20\_1: Version 0.20.1 ============== \\November 21, 2018\\ 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 to 0.20.1. 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:`decomposition.IncrementalPCA` (bug fix) Changelog --------- :mod:`sklearn.cluster` ...................... - \|Efficiency\| make :class:`cluster.MeanShift` no longer try to do nested parallelism as the overhead would hurt performance significantly when ``n\_jobs > 1``. :issue:`12159` by :user:`Olivier Grisel `. - \|Fix\| Fixed a bug in :class:`cluster.DBSCAN` with precomputed sparse neighbors graph, which would add explicitly zeros on the diagonal even when already present. :issue:`12105` by `Tom Dupre la Tour`\_. :mod:`sklearn.compose` ...................... - \|Fix\| Fixed an issue in :class:`compose.ColumnTransformer` when stacking columns with types not convertible to a numeric. :issue:`11912` by :user:`Adrin Jalali `. - \|API\| :class:`compose.ColumnTransformer` now applies the ``sparse\_threshold`` even if all transformation results are sparse. :issue:`12304` by `Andreas Müller`\_. - \|API\| :func:`compose.make\_column\_transformer` now expects ``(transformer, columns)`` instead of ``(columns, transformer)`` to keep consistent with :class:`compose.ColumnTransformer`. :issue:`12339` by :user:`Adrin Jalali `. :mod:`sklearn.datasets` ............................ - \|Fix\| :func:`datasets.fetch\_openml` to correctly use the local cache. :issue:`12246` by :user:`Jan N. van Rijn `. - \|Fix\| :func:`datasets.fetch\_openml` to correctly handle ignore attributes and row	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.0695575475692749, 0.03172123432159424, -0.07771633565425873, -0.07108277082443237, -0.0617176853120327, -0.028298532590270042, -0.01668468303978443, 0.04325141757726669, -0.05401117727160454, 0.003716542851179838, 0.04512723535299301, -0.005774934310466051, 0.05841897800564766, -0.04662...	0.136674
Müller`\_. - \|API\| :func:`compose.make\_column\_transformer` now expects ``(transformer, columns)`` instead of ``(columns, transformer)`` to keep consistent with :class:`compose.ColumnTransformer`. :issue:`12339` by :user:`Adrin Jalali `. :mod:`sklearn.datasets` ............................ - \|Fix\| :func:`datasets.fetch\_openml` to correctly use the local cache. :issue:`12246` by :user:`Jan N. van Rijn `. - \|Fix\| :func:`datasets.fetch\_openml` to correctly handle ignore attributes and row id attributes. :issue:`12330` by :user:`Jan N. van Rijn `. - \|Fix\| Fixed integer overflow in :func:`datasets.make\_classification` for values of ``n\_informative`` parameter larger than 64. :issue:`10811` by :user:`Roman Feldbauer `. - \|Fix\| Fixed olivetti faces dataset ``DESCR`` attribute to point to the right location in :func:`datasets.fetch\_olivetti\_faces`. :issue:`12441` by :user:`Jérémie du Boisberranger ` - \|Fix\| :func:`datasets.fetch\_openml` to retry downloading when reading from local cache fails. :issue:`12517` by :user:`Thomas Fan `. :mod:`sklearn.decomposition` ............................ - \|Fix\| Fixed a regression in :class:`decomposition.IncrementalPCA` where 0.20.0 raised an error if the number of samples in the final batch for fitting IncrementalPCA was smaller than n\_components. :issue:`12234` by :user:`Ming Li `. :mod:`sklearn.ensemble` ....................... - \|Fix\| Fixed a bug mostly affecting :class:`ensemble.RandomForestClassifier` where ``class\_weight='balanced\_subsample'`` failed with more than 32 classes. :issue:`12165` by `Joel Nothman`\_. - \|Fix\| Fixed a bug affecting :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` and :class:`ensemble.IsolationForest`, where ``max\_features`` was sometimes rounded down to zero. :issue:`12388` by :user:`Connor Tann `. :mod:`sklearn.feature\_extraction` .................................. - \|Fix\| Fixed a regression in v0.20.0 where :func:`feature\_extraction.text.CountVectorizer` and other text vectorizers could error during stop words validation with custom preprocessors or tokenizers. :issue:`12393` by `Roman Yurchak`\_. :mod:`sklearn.linear\_model` ........................... - \|Fix\| :class:`linear\_model.SGDClassifier` and variants with ``early\_stopping=True`` would not use a consistent validation split in the multiclass case and this would cause a crash when using those estimators as part of parallel parameter search or cross-validation. :issue:`12122` by :user:`Olivier Grisel `. - \|Fix\| Fixed a bug affecting :class:`linear\_model.SGDClassifier` in the multiclass case. Each one-versus-all step is run in a :class:`joblib.Parallel` call and mutating a common parameter, causing a segmentation fault if called within a backend using processes and not threads. We now use ``require=sharedmem`` at the :class:`joblib.Parallel` instance creation. :issue:`12518` by :user:`Pierre Glaser ` and :user:`Olivier Grisel `. :mod:`sklearn.metrics` ...................... - \|Fix\| Fixed a bug in `metrics.pairwise.pairwise\_distances\_argmin\_min` which returned the square root of the distance when the metric parameter was set to "euclidean". :issue:`12481` by :user:`Jérémie du Boisberranger `. - \|Fix\| Fixed a bug in `metrics.pairwise.pairwise\_distances\_chunked` which didn't ensure the diagonal is zero for euclidean distances. :issue:`12612` by :user:`Andreas Müller `. - \|API\| The `metrics.calinski\_harabaz\_score` has been renamed to :func:`metrics.calinski\_harabasz\_score` and will be removed in version 0.23. :issue:`12211` by :user:`Lisa Thomas `, :user:`Mark Hannel ` and :user:`Melissa Ferrari `. :mod:`sklearn.mixture` ........................ - \|Fix\| Ensure that the ``fit\_predict`` method of :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture` always yield assignments consistent with ``fit`` followed by ``predict`` even if the convergence criterion is too loose or not met. :issue:`12451` by :user:`Olivier Grisel `. :mod:`sklearn.neighbors` ........................ - \|Fix\| force the parallelism backend to :code:`threading` for :class:`neighbors.KDTree` and :class:`neighbors.BallTree` in Python 2.7 to avoid pickling errors caused by the serialization of their methods. :issue:`12171` by :user:`Thomas Moreau `. :mod:`sklearn.preprocessing` ............................. - \|Fix\| Fixed bug in :class:`preprocessing.OrdinalEncoder` when passing manually specified categories. :issue:`12365` by `Joris Van den Bossche`\_. - \|Fix\| Fixed bug in :class:`preprocessing.KBinsDiscretizer` where the ``transform`` method mutates the ``\_encoder`` attribute. The ``transform`` method is now thread safe. :issue:`12514` by :user:`Hanmin Qin `. - \|Fix\| Fixed a bug in :class:`preprocessing.PowerTransformer` where the Yeo-Johnson transform was incorrect for lambda parameters outside of `[0, 2]` :issue:`12522` by :user:`Nicolas Hug`. - \|Fix\| Fixed a bug in :class:`preprocessing.OneHotEncoder` where transform failed when set to ignore unknown numpy strings of different lengths :issue:`12471` by :user:`Gabriel Marzinotto`. - \|API\| The default value of the :code:`method` argument in :func:`preprocessing.power\_transform` will be changed from :code:`box-cox` to :code:`yeo-johnson` to match :class:`preprocessing.PowerTransformer` in version 0.23. A FutureWarning is	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.06744640320539474, 0.03301984816789627, -0.08236118406057358, -0.01903645507991314, -0.011522280983626842, -0.11198733747005463, 0.0170151200145483, 0.04496707394719124, -0.04167640954256058, -0.005405099131166935, 0.04493090137839317, -0.029991276562213898, -0.01137771550565958, -0.086...	-0.011523
\|Fix\| Fixed a bug in :class:`preprocessing.OneHotEncoder` where transform failed when set to ignore unknown numpy strings of different lengths :issue:`12471` by :user:`Gabriel Marzinotto`. - \|API\| The default value of the :code:`method` argument in :func:`preprocessing.power\_transform` will be changed from :code:`box-cox` to :code:`yeo-johnson` to match :class:`preprocessing.PowerTransformer` in version 0.23. A FutureWarning is raised when the default value is used. :issue:`12317` by :user:`Eric Chang `. :mod:`sklearn.utils` ........................ - \|Fix\| Use float64 for mean accumulator to avoid floating point precision issues in :class:`preprocessing.StandardScaler` and :class:`decomposition.IncrementalPCA` when using float32 datasets. :issue:`12338` by :user:`bauks `. - \|Fix\| Calling :func:`utils.check\_array` on `pandas.Series`, which raised an error in 0.20.0, now returns the expected output again. :issue:`12625` by `Andreas Müller`\_ Miscellaneous ............. - \|Fix\| When using site joblib by setting the environment variable `SKLEARN\_SITE\_JOBLIB`, added compatibility with joblib 0.11 in addition to 0.12+. :issue:`12350` by `Joel Nothman`\_ and `Roman Yurchak`\_. - \|Fix\| Make sure to avoid raising ``FutureWarning`` when calling ``np.vstack`` with numpy 1.16 and later (use list comprehensions instead of generator expressions in many locations of the scikit-learn code base). :issue:`12467` by :user:`Olivier Grisel `. - \|API\| Removed all mentions of ``sklearn.externals.joblib``, and deprecated joblib methods exposed in ``sklearn.utils``, except for `utils.parallel\_backend` and `utils.register\_parallel\_backend`, which allow users to configure parallel computation in scikit-learn. Other functionalities are part of `joblib `\_. package and should be used directly, by installing it. The goal of this change is to prepare for unvendoring joblib in future version of scikit-learn. :issue:`12345` by :user:`Thomas Moreau ` Code and Documentation Contributors ----------------------------------- With thanks to: ^\_\_^, Adrin Jalali, Andrea Navarrete, Andreas Mueller, bauks, BenjaStudio, Cheuk Ting Ho, Connossor, Corey Levinson, Dan Stine, daten-kieker, Denis Kataev, Dillon Gardner, Dmitry Vukolov, Dougal J. Sutherland, Edward J Brown, Eric Chang, Federico Caselli, Gabriel Marzinotto, Gael Varoquaux, GauravAhlawat, Gustavo De Mari Pereira, Hanmin Qin, haroldfox, JackLangerman, Jacopo Notarstefano, janvanrijn, jdethurens, jeremiedbb, Joel Nothman, Joris Van den Bossche, Koen, Kushal Chauhan, Lee Yi Jie Joel, Lily Xiong, mail-liam, Mark Hannel, melsyt, Ming Li, Nicholas Smith, Nicolas Hug, Nikolay Shebanov, Oleksandr Pavlyk, Olivier Grisel, Peter Hausamann, Pierre Glaser, Pulkit Maloo, Quentin Batista, Radostin Stoyanov, Ramil Nugmanov, Rebekah Kim, Reshama Shaikh, Rohan Singh, Roman Feldbauer, Roman Yurchak, Roopam Sharma, Sam Waterbury, Scott Lowe, Sebastian Raschka, Stephen Tierney, SylvainLan, TakingItCasual, Thomas Fan, Thomas Moreau, Tom Dupré la Tour, Tulio Casagrande, Utkarsh Upadhyay, Xing Han Lu, Yaroslav Halchenko, Zach Miller .. \_changes\_0\_20: Version 0.20.0 ============== \\September 25, 2018\\ This release packs in a mountain of bug fixes, features and enhancements for the Scikit-learn library, and improvements to the documentation and examples. Thanks to our contributors! This release is dedicated to the memory of Raghav Rajagopalan. Highlights ---------- We have tried to improve our support for common data-science use-cases including missing values, categorical variables, heterogeneous data, and features/targets with unusual distributions. Missing values in features, represented by NaNs, are now accepted in column-wise preprocessing such as scalers. Each feature is fitted disregarding NaNs, and data containing NaNs can be transformed. The new :mod:`sklearn.impute` module provides estimators for learning despite missing data. :class:`~compose.ColumnTransformer` handles the case where different features or columns of a pandas.DataFrame need different preprocessing. String or pandas Categorical columns can now be encoded with :class:`~preprocessing.OneHotEncoder` or :class:`~preprocessing.OrdinalEncoder`. :class:`~compose.TransformedTargetRegressor` helps when the regression target needs to be transformed to be modeled. :class:`~preprocessing.PowerTransformer` and :class:`~preprocessing.KBinsDiscretizer` join :class:`~preprocessing.QuantileTransformer` as non-linear transformations. Beyond this, we have added :term:`sample\_weight` support to several estimators (including :class:`~cluster.KMeans`, :class:`~linear\_model.BayesianRidge` and :class:`~neighbors.KernelDensity`) and improved stopping criteria in others (including :class:`~neural\_network.MLPRegressor`, :class:`~ensemble.GradientBoostingRegressor` and :class:`~linear\_model.SGDRegressor`). This release is also the first to be accompanied by a :ref:`glossary` developed by `Joel Nothman`\_. The glossary is a reference resource to help users and contributors become familiar	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.1132381334900856, 0.06248026713728905, -0.11412513256072998, -0.007573227863758802, -0.07255852222442627, -0.06273515522480011, -0.0663403794169426, 0.057978324592113495, -0.04858675226569176, -0.01996036060154438, 0.07292841374874115, -0.06915430724620819, -0.0024232473224401474, -0.00...	0.032898
:term:`sample\_weight` support to several estimators (including :class:`~cluster.KMeans`, :class:`~linear\_model.BayesianRidge` and :class:`~neighbors.KernelDensity`) and improved stopping criteria in others (including :class:`~neural\_network.MLPRegressor`, :class:`~ensemble.GradientBoostingRegressor` and :class:`~linear\_model.SGDRegressor`). This release is also the first to be accompanied by a :ref:`glossary` developed by `Joel Nothman`\_. The glossary is a reference resource to help users and contributors become familiar with the terminology and conventions used in Scikit-learn. Sorry if your contribution didn't make it into the highlights. There's a lot here... 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:`cluster.MeanShift` (bug fix) - :class:`decomposition.IncrementalPCA` in Python 2 (bug fix) - :class:`decomposition.SparsePCA` (bug fix) - :class:`ensemble.GradientBoostingClassifier` (bug fix affecting feature importances) - :class:`isotonic.IsotonicRegression` (bug fix) - :class:`linear\_model.ARDRegression` (bug fix) - :class:`linear\_model.LogisticRegressionCV` (bug fix) - :class:`linear\_model.OrthogonalMatchingPursuit` (bug fix) - :class:`linear\_model.PassiveAggressiveClassifier` (bug fix) - :class:`linear\_model.PassiveAggressiveRegressor` (bug fix) - :class:`linear\_model.Perceptron` (bug fix) - :class:`linear\_model.SGDClassifier` (bug fix) - :class:`linear\_model.SGDRegressor` (bug fix) - :class:`metrics.roc\_auc\_score` (bug fix) - :class:`metrics.roc\_curve` (bug fix) - `neural\_network.BaseMultilayerPerceptron` (bug fix) - :class:`neural\_network.MLPClassifier` (bug fix) - :class:`neural\_network.MLPRegressor` (bug fix) - The v0.19.0 release notes failed to mention a backwards incompatibility with :class:`model\_selection.StratifiedKFold` when ``shuffle=True`` due to :issue:`7823`. 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 ---------------- \* :issue:`11924`: :class:`linear\_model.LogisticRegressionCV` with `solver='lbfgs'` and `multi\_class='multinomial'` may be non-deterministic or otherwise broken on macOS. This appears to be the case on Travis CI servers, but has not been confirmed on personal MacBooks! This issue has been present in previous releases. \* :issue:`9354`: :func:`metrics.pairwise.euclidean\_distances` (which is used several times throughout the library) gives results with poor precision, which particularly affects its use with 32-bit float inputs. This became more problematic in versions 0.18 and 0.19 when some algorithms were changed to avoid casting 32-bit data into 64-bit. Changelog --------- Support for Python 3.3 has been officially dropped. :mod:`sklearn.cluster` ...................... - \|MajorFeature\| :class:`cluster.AgglomerativeClustering` now supports Single Linkage clustering via ``linkage='single'``. :issue:`9372` by :user:`Leland McInnes ` and :user:`Steve Astels `. - \|Feature\| :class:`cluster.KMeans` and :class:`cluster.MiniBatchKMeans` now support sample weights via new parameter ``sample\_weight`` in ``fit`` function. :issue:`10933` by :user:`Johannes Hansen `. - \|Efficiency\| :class:`cluster.KMeans`, :class:`cluster.MiniBatchKMeans` and :func:`cluster.k\_means` passed with ``algorithm='full'`` now enforce row-major ordering, improving runtime. :issue:`10471` by :user:`Gaurav Dhingra `. - \|Efficiency\| :class:`cluster.DBSCAN` now is parallelized according to ``n\_jobs`` regardless of ``algorithm``. :issue:`8003` by :user:`Joël Billaud `. - \|Enhancement\| :class:`cluster.KMeans` now gives a warning if the number of distinct clusters found is smaller than ``n\_clusters``. This may occur when the number of distinct points in the data set is actually smaller than the number of cluster one is looking for. :issue:`10059` by :user:`Christian Braune `. - \|Fix\| Fixed a bug where the ``fit`` method of :class:`cluster.AffinityPropagation` stored cluster centers as 3d array instead of 2d array in case of non-convergence. For the same class, fixed undefined and arbitrary behavior in case of training data where all samples had equal similarity. :issue:`9612`. By :user:`Jonatan Samoocha `. - \|Fix\| Fixed a bug in :func:`cluster.spectral\_clustering` where the normalization of the spectrum was using a division instead of a multiplication. :issue:`8129` by :user:`Jan Margeta `, :user:`Guillaume Lemaitre `, and :user:`Devansh D. `. - \|Fix\| Fixed a bug in `cluster.k\_means\_elkan` where the returned ``iteration`` was 1 less than the correct value. Also added the missing ``n\_iter\_`` attribute in the docstring of :class:`cluster.KMeans`. :issue:`11353` by :user:`Jeremie du Boisberranger `. - \|Fix\| Fixed a bug in :func:`cluster.mean\_shift` where the assigned labels were not deterministic if there	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.09338432550430298, -0.08295569568872452, 0.04336114972829819, 0.055277228355407715, 0.05679180845618248, 0.005782104562968016, -0.0014809536514803767, 0.024354659020900726, 0.01298479177057743, 0.007531462702900171, 0.039185311645269394, -0.021300343796610832, 0.023865722119808197, -0.1...	0.178514
\|Fix\| Fixed a bug in `cluster.k\_means\_elkan` where the returned ``iteration`` was 1 less than the correct value. Also added the missing ``n\_iter\_`` attribute in the docstring of :class:`cluster.KMeans`. :issue:`11353` by :user:`Jeremie du Boisberranger `. - \|Fix\| Fixed a bug in :func:`cluster.mean\_shift` where the assigned labels were not deterministic if there were multiple clusters with the same intensities. :issue:`11901` by :user:`Adrin Jalali `. - \|API\| Deprecate ``pooling\_func`` unused parameter in :class:`cluster.AgglomerativeClustering`. :issue:`9875` by :user:`Kumar Ashutosh `. :mod:`sklearn.compose` ...................... - New module. - \|MajorFeature\| Added :class:`compose.ColumnTransformer`, which allows to apply different transformers to different columns of arrays or pandas DataFrames. :issue:`9012` by `Andreas Müller`\_ and `Joris Van den Bossche`\_, and :issue:`11315` by :user:`Thomas Fan `. - \|MajorFeature\| Added the :class:`compose.TransformedTargetRegressor` which transforms the target y before fitting a regression model. The predictions are mapped back to the original space via an inverse transform. :issue:`9041` by `Andreas Müller`\_ and :user:`Guillaume Lemaitre `. :mod:`sklearn.covariance` ......................... - \|Efficiency\| Runtime improvements to :class:`covariance.GraphicalLasso`. :issue:`9858` by :user:`Steven Brown `. - \|API\| The `covariance.graph\_lasso`, `covariance.GraphLasso` and `covariance.GraphLassoCV` have been renamed to :func:`covariance.graphical\_lasso`, :class:`covariance.GraphicalLasso` and :class:`covariance.GraphicalLassoCV` respectively and will be removed in version 0.22. :issue:`9993` by :user:`Artiem Krinitsyn ` :mod:`sklearn.datasets` ....................... - \|MajorFeature\| Added :func:`datasets.fetch\_openml` to fetch datasets from `OpenML `\_. OpenML is a free, open data sharing platform and will be used instead of mldata as it provides better service availability. :issue:`9908` by `Andreas Müller`\_ and :user:`Jan N. van Rijn `. - \|Feature\| In :func:`datasets.make\_blobs`, one can now pass a list to the ``n\_samples`` parameter to indicate the number of samples to generate per cluster. :issue:`8617` by :user:`Maskani Filali Mohamed ` and :user:`Konstantinos Katrioplas `. - \|Feature\| Add ``filename`` attribute to :mod:`sklearn.datasets` that have a CSV file. :issue:`9101` by :user:`alex-33 ` and :user:`Maskani Filali Mohamed `. - \|Feature\| ``return\_X\_y`` parameter has been added to several dataset loaders. :issue:`10774` by :user:`Chris Catalfo `. - \|Fix\| Fixed a bug in `datasets.load\_boston` which had a wrong data point. :issue:`10795` by :user:`Takeshi Yoshizawa `. - \|Fix\| Fixed a bug in :func:`datasets.load\_iris` which had two wrong data points. :issue:`11082` by :user:`Sadhana Srinivasan ` and :user:`Hanmin Qin `. - \|Fix\| Fixed a bug in :func:`datasets.fetch\_kddcup99`, where data were not properly shuffled. :issue:`9731` by `Nicolas Goix`\_. - \|Fix\| Fixed a bug in :func:`datasets.make\_circles`, where no odd number of data points could be generated. :issue:`10045` by :user:`Christian Braune `. - \|API\| Deprecated `sklearn.datasets.fetch\_mldata` to be removed in version 0.22. mldata.org is no longer operational. Until removal it will remain possible to load cached datasets. :issue:`11466` by `Joel Nothman`\_. :mod:`sklearn.decomposition` ............................ - \|Feature\| :func:`decomposition.dict\_learning` functions and models now support positivity constraints. This applies to the dictionary and sparse code. :issue:`6374` by :user:`John Kirkham `. - \|Feature\| \|Fix\| :class:`decomposition.SparsePCA` now exposes ``normalize\_components``. When set to True, the train and test data are centered with the train mean respectively during the fit phase and the transform phase. This fixes the behavior of SparsePCA. When set to False, which is the default, the previous abnormal behaviour still holds. The False value is for backward compatibility and should not be used. :issue:`11585` by :user:`Ivan Panico `. - \|Efficiency\| Efficiency improvements in :func:`decomposition.dict\_learning`. :issue:`11420` and others by :user:`John Kirkham `. - \|Fix\| Fix for uninformative error in :class:`decomposition.IncrementalPCA`: now an error is raised if the number of components is larger than the chosen batch size. The ``n\_components=None`` case was adapted accordingly. :issue:`6452`. By :user:`Wally Gauze `. - \|Fix\| Fixed a bug where the ``partial\_fit`` method of :class:`decomposition.IncrementalPCA` used integer division instead of float division on Python 2. :issue:`9492` by :user:`James Bourbeau `. - \|Fix\| In :class:`decomposition.PCA` selecting a n\_components parameter greater than the number of samples now raises an error. Similarly, the	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ 0.013605649583041668, -0.007987613789737225, -0.04668266326189041, 0.03312239423394203, 0.03631706163287163, -0.012002368457615376, 0.011166783049702644, 0.007727031596004963, -0.03774194419384003, -0.013762323185801506, 0.08239094167947769, -0.10650277137756348, 0.025092516094446182, -0.0...	0.175499
By :user:`Wally Gauze `. - \|Fix\| Fixed a bug where the ``partial\_fit`` method of :class:`decomposition.IncrementalPCA` used integer division instead of float division on Python 2. :issue:`9492` by :user:`James Bourbeau `. - \|Fix\| In :class:`decomposition.PCA` selecting a n\_components parameter greater than the number of samples now raises an error. Similarly, the ``n\_components=None`` case now selects the minimum of ``n\_samples`` and ``n\_features``. :issue:`8484` by :user:`Wally Gauze `. - \|Fix\| Fixed a bug in :class:`decomposition.PCA` where users will get unexpected error with large datasets when ``n\_components='mle'`` on Python 3 versions. :issue:`9886` by :user:`Hanmin Qin `. - \|Fix\| Fixed an underflow in calculating KL-divergence for :class:`decomposition.NMF` :issue:`10142` by `Tom Dupre la Tour`\_. - \|Fix\| Fixed a bug in :class:`decomposition.SparseCoder` when running OMP sparse coding in parallel using read-only memory mapped datastructures. :issue:`5956` by :user:`Vighnesh Birodkar ` and :user:`Olivier Grisel `. :mod:`sklearn.discriminant\_analysis` .................................... - \|Efficiency\| Memory usage improvement for `\_class\_means` and `\_class\_cov` in :mod:`sklearn.discriminant\_analysis`. :issue:`10898` by :user:`Nanxin Chen `. :mod:`sklearn.dummy` .................... - \|Feature\| :class:`dummy.DummyRegressor` now has a ``return\_std`` option in its ``predict`` method. The returned standard deviations will be zeros. - \|Feature\| :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor` now only require X to be an object with finite length or shape. :issue:`9832` by :user:`Vrishank Bhardwaj `. - \|Feature\| :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor` can now be scored without supplying test samples. :issue:`11951` by :user:`Rüdiger Busche `. :mod:`sklearn.ensemble` ....................... - \|Feature\| :class:`ensemble.BaggingRegressor` and :class:`ensemble.BaggingClassifier` can now be fit with missing/non-finite values in X and/or multi-output Y to support wrapping pipelines that perform their own imputation. :issue:`9707` by :user:`Jimmy Wan `. - \|Feature\| :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` now support early stopping via ``n\_iter\_no\_change``, ``validation\_fraction`` and ``tol``. :issue:`7071` by `Raghav RV`\_ - \|Feature\| Added ``named\_estimators\_`` parameter in :class:`ensemble.VotingClassifier` to access fitted estimators. :issue:`9157` by :user:`Herilalaina Rakotoarison `. - \|Fix\| Fixed a bug when fitting :class:`ensemble.GradientBoostingClassifier` or :class:`ensemble.GradientBoostingRegressor` with ``warm\_start=True`` which previously raised a segmentation fault due to a non-conversion of CSC matrix into CSR format expected by ``decision\_function``. Similarly, Fortran-ordered arrays are converted to C-ordered arrays in the dense case. :issue:`9991` by :user:`Guillaume Lemaitre `. - \|Fix\| Fixed a bug in :class:`ensemble.GradientBoostingRegressor` and :class:`ensemble.GradientBoostingClassifier` to have feature importances summed and then normalized, rather than normalizing on a per-tree basis. The previous behavior over-weighted the Gini importance of features that appear in later stages. This issue only affected feature importances. :issue:`11176` by :user:`Gil Forsyth `. - \|API\| The default value of the ``n\_estimators`` parameter of :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`, and :class:`ensemble.RandomTreesEmbedding` will change from 10 in version 0.20 to 100 in 0.22. A FutureWarning is raised when the default value is used. :issue:`11542` by :user:`Anna Ayzenshtat `. - \|API\| Classes derived from `ensemble.BaseBagging`. The attribute ``estimators\_samples\_`` will return a list of arrays containing the indices selected for each bootstrap instead of a list of arrays containing the mask of the samples selected for each bootstrap. Indices allows to repeat samples while mask does not allow this functionality. :issue:`9524` by :user:`Guillaume Lemaitre `. - \|Fix\| `ensemble.BaseBagging` where one could not deterministically reproduce ``fit`` result using the object attributes when ``random\_state`` is set. :issue:`9723` by :user:`Guillaume Lemaitre `. :mod:`sklearn.feature\_extraction` ................................. - \|Feature\| Enable the call to `get\_feature\_names` in unfitted :class:`feature\_extraction.text.CountVectorizer` initialized with a vocabulary. :issue:`10908` by :user:`Mohamed Maskani `. - \|Enhancement\| ``idf\_`` can now be set on a :class:`feature\_extraction.text.TfidfTransformer`. :issue:`10899` by :user:`Sergey Melderis `. - \|Fix\| Fixed a bug in :func:`feature\_extraction.image.extract\_patches\_2d` which would throw an exception if ``max\_patches`` was greater than or equal to the number of all possible patches rather than simply returning the number of possible patches. :issue:`10101` by :user:`Varun Agrawal ` - \|Fix\| Fixed a bug in :class:`feature\_extraction.text.CountVectorizer`, :class:`feature\_extraction.text.TfidfVectorizer`, :class:`feature\_extraction.text.HashingVectorizer` to support 64 bit sparse array indexing necessary to process large datasets with more	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.09498418122529984, -0.03171662241220474, 0.04254479333758354, -0.01815570890903473, 0.00513349287211895, -0.13535216450691223, -0.035929545760154724, 0.011401877738535404, -0.0444544292986393, -0.03901444002985954, 0.02836211957037449, -0.03253433480858803, -0.004831585101783276, -0.047...	-0.068601
``max\_patches`` was greater than or equal to the number of all possible patches rather than simply returning the number of possible patches. :issue:`10101` by :user:`Varun Agrawal ` - \|Fix\| Fixed a bug in :class:`feature\_extraction.text.CountVectorizer`, :class:`feature\_extraction.text.TfidfVectorizer`, :class:`feature\_extraction.text.HashingVectorizer` to support 64 bit sparse array indexing necessary to process large datasets with more than 2·10⁹ tokens (words or n-grams). :issue:`9147` by :user:`Claes-Fredrik Mannby ` and `Roman Yurchak`\_. - \|Fix\| Fixed bug in :class:`feature\_extraction.text.TfidfVectorizer` which was ignoring the parameter ``dtype``. In addition, :class:`feature\_extraction.text.TfidfTransformer` will preserve ``dtype`` for floating and raise a warning if ``dtype`` requested is integer. :issue:`10441` by :user:`Mayur Kulkarni ` and :user:`Guillaume Lemaitre `. :mod:`sklearn.feature\_selection` ................................ - \|Feature\| Added select K best features functionality to :class:`feature\_selection.SelectFromModel`. :issue:`6689` by :user:`Nihar Sheth ` and :user:`Quazi Rahman `. - \|Feature\| Added ``min\_features\_to\_select`` parameter to :class:`feature\_selection.RFECV` to bound evaluated features counts. :issue:`11293` by :user:`Brent Yi `. - \|Feature\| :class:`feature\_selection.RFECV`'s fit method now supports :term:`groups`. :issue:`9656` by :user:`Adam Greenhall `. - \|Fix\| Fixed computation of ``n\_features\_to\_compute`` for edge case with tied CV scores in :class:`feature\_selection.RFECV`. :issue:`9222` by :user:`Nick Hoh `. :mod:`sklearn.gaussian\_process` ............................... - \|Efficiency\| In :class:`gaussian\_process.GaussianProcessRegressor`, method ``predict`` is faster when using ``return\_std=True`` in particular more when called several times in a row. :issue:`9234` by :user:`andrewww ` and :user:`Minghui Liu `. :mod:`sklearn.impute` ..................... - New module, adopting ``preprocessing.Imputer`` as :class:`impute.SimpleImputer` with minor changes (see under preprocessing below). - \|MajorFeature\| Added :class:`impute.MissingIndicator` which generates a binary indicator for missing values. :issue:`8075` by :user:`Maniteja Nandana ` and :user:`Guillaume Lemaitre `. - \|Feature\| The :class:`impute.SimpleImputer` has a new strategy, ``'constant'``, to complete missing values with a fixed one, given by the ``fill\_value`` parameter. This strategy supports numeric and non-numeric data, and so does the ``'most\_frequent'`` strategy now. :issue:`11211` by :user:`Jeremie du Boisberranger `. :mod:`sklearn.isotonic` ....................... - \|Fix\| Fixed a bug in :class:`isotonic.IsotonicRegression` which incorrectly combined weights when fitting a model to data involving points with identical X values. :issue:`9484` by :user:`Dallas Card ` :mod:`sklearn.linear\_model` ........................... - \|Feature\| :class:`linear\_model.SGDClassifier`, :class:`linear\_model.SGDRegressor`, :class:`linear\_model.PassiveAggressiveClassifier`, :class:`linear\_model.PassiveAggressiveRegressor` and :class:`linear\_model.Perceptron` now expose ``early\_stopping``, ``validation\_fraction`` and ``n\_iter\_no\_change`` parameters, to stop optimization monitoring the score on a validation set. A new learning rate ``"adaptive"`` strategy divides the learning rate by 5 each time ``n\_iter\_no\_change`` consecutive epochs fail to improve the model. :issue:`9043` by `Tom Dupre la Tour`\_. - \|Feature\| Add `sample\_weight` parameter to the fit method of :class:`linear\_model.BayesianRidge` for weighted linear regression. :issue:`10112` by :user:`Peter St. John `. - \|Fix\| Fixed a bug in `logistic.logistic\_regression\_path` to ensure that the returned coefficients are correct when ``multiclass='multinomial'``. Previously, some of the coefficients would override each other, leading to incorrect results in :class:`linear\_model.LogisticRegressionCV`. :issue:`11724` by :user:`Nicolas Hug `. - \|Fix\| Fixed a bug in :class:`linear\_model.LogisticRegression` where when using the parameter ``multi\_class='multinomial'``, the ``predict\_proba`` method was returning incorrect probabilities in the case of binary outcomes. :issue:`9939` by :user:`Roger Westover `. - \|Fix\| Fixed a bug in :class:`linear\_model.LogisticRegressionCV` where the ``score`` method always computes accuracy, not the metric given by the ``scoring`` parameter. :issue:`10998` by :user:`Thomas Fan `. - \|Fix\| Fixed a bug in :class:`linear\_model.LogisticRegressionCV` where the 'ovr' strategy was always used to compute cross-validation scores in the multiclass setting, even if ``'multinomial'`` was set. :issue:`8720` by :user:`William de Vazelhes `. - \|Fix\| Fixed a bug in :class:`linear\_model.OrthogonalMatchingPursuit` that was broken when setting ``normalize=False``. :issue:`10071` by `Alexandre Gramfort`\_. - \|Fix\| Fixed a bug in :class:`linear\_model.ARDRegression` which caused incorrectly updated estimates for the standard deviation and the coefficients. :issue:`10153` by :user:`Jörg Döpfert `. - \|Fix\| Fixed a bug in :class:`linear\_model.ARDRegression` and :class:`linear\_model.BayesianRidge` which caused NaN predictions when fitted with a constant target. :issue:`10095` by :user:`Jörg Döpfert `. - \|Fix\| Fixed a bug in :class:`linear\_model.RidgeClassifierCV` where the parameter ``store\_cv\_values`` was not implemented though it was documented	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.024588800966739655, 0.013293702155351639, 0.01623401790857315, -0.05297107249498367, -0.0302361398935318, -0.046238478273153305, 0.036461975425481796, 0.0583786703646183, -0.08636080473661423, -0.015818925574421883, 0.04352576658129692, -0.017239276319742203, 0.06018037348985672, -0.068...	0.039069
and the coefficients. :issue:`10153` by :user:`Jörg Döpfert `. - \|Fix\| Fixed a bug in :class:`linear\_model.ARDRegression` and :class:`linear\_model.BayesianRidge` which caused NaN predictions when fitted with a constant target. :issue:`10095` by :user:`Jörg Döpfert `. - \|Fix\| Fixed a bug in :class:`linear\_model.RidgeClassifierCV` where the parameter ``store\_cv\_values`` was not implemented though it was documented in ``cv\_values`` as a way to set up the storage of cross-validation values for different alphas. :issue:`10297` by :user:`Mabel Villalba-Jiménez `. - \|Fix\| Fixed a bug in :class:`linear\_model.ElasticNet` which caused the input to be overridden when using parameter ``copy\_X=True`` and ``check\_input=False``. :issue:`10581` by :user:`Yacine Mazari `. - \|Fix\| Fixed a bug in :class:`sklearn.linear\_model.Lasso` where the coefficient had wrong shape when ``fit\_intercept=False``. :issue:`10687` by :user:`Martin Hahn `. - \|Fix\| Fixed a bug in :func:`sklearn.linear\_model.LogisticRegression` where the ``multi\_class='multinomial'`` with binary output ``with warm\_start=True`` :issue:`10836` by :user:`Aishwarya Srinivasan `. - \|Fix\| Fixed a bug in :class:`linear\_model.RidgeCV` where using integer ``alphas`` raised an error. :issue:`10397` by :user:`Mabel Villalba-Jiménez `. - \|Fix\| Fixed condition triggering gap computation in :class:`linear\_model.Lasso` and :class:`linear\_model.ElasticNet` when working with sparse matrices. :issue:`10992` by `Alexandre Gramfort`\_. - \|Fix\| Fixed a bug in :class:`linear\_model.SGDClassifier`, :class:`linear\_model.SGDRegressor`, :class:`linear\_model.PassiveAggressiveClassifier`, :class:`linear\_model.PassiveAggressiveRegressor` and :class:`linear\_model.Perceptron`, where the stopping criterion was stopping the algorithm before convergence. A parameter ``n\_iter\_no\_change`` was added and set by default to 5. Previous behavior is equivalent to setting the parameter to 1. :issue:`9043` by `Tom Dupre la Tour`\_. - \|Fix\| Fixed a bug where liblinear and libsvm-based estimators would segfault if passed a scipy.sparse matrix with 64-bit indices. They now raise a ValueError. :issue:`11327` by :user:`Karan Dhingra ` and `Joel Nothman`\_. - \|API\| The default values of the ``solver`` and ``multi\_class`` parameters of :class:`linear\_model.LogisticRegression` will change respectively from ``'liblinear'`` and ``'ovr'`` in version 0.20 to ``'lbfgs'`` and ``'auto'`` in version 0.22. A FutureWarning is raised when the default values are used. :issue:`11905` by `Tom Dupre la Tour`\_ and `Joel Nothman`\_. - \|API\| Deprecate ``positive=True`` option in :class:`linear\_model.Lars` as the underlying implementation is broken. Use :class:`linear\_model.Lasso` instead. :issue:`9837` by `Alexandre Gramfort`\_. - \|API\| ``n\_iter\_`` may vary from previous releases in :class:`linear\_model.LogisticRegression` with ``solver='lbfgs'`` and :class:`linear\_model.HuberRegressor`. For Scipy <= 1.0.0, the optimizer could perform more than the requested maximum number of iterations. Now both estimators will report at most ``max\_iter`` iterations even if more were performed. :issue:`10723` by `Joel Nothman`\_. :mod:`sklearn.manifold` ....................... - \|Efficiency\| Speed improvements for both 'exact' and 'barnes\_hut' methods in :class:`manifold.TSNE`. :issue:`10593` and :issue:`10610` by `Tom Dupre la Tour`\_. - \|Feature\| Support sparse input in :meth:`manifold.Isomap.fit`. :issue:`8554` by :user:`Leland McInnes `. - \|Feature\| `manifold.t\_sne.trustworthiness` accepts metrics other than Euclidean. :issue:`9775` by :user:`William de Vazelhes `. - \|Fix\| Fixed a bug in :func:`manifold.spectral\_embedding` where the normalization of the spectrum was using a division instead of a multiplication. :issue:`8129` by :user:`Jan Margeta `, :user:`Guillaume Lemaitre `, and :user:`Devansh D. `. - \|API\| \|Feature\| Deprecate ``precomputed`` parameter in function `manifold.t\_sne.trustworthiness`. Instead, the new parameter ``metric`` should be used with any compatible metric including 'precomputed', in which case the input matrix ``X`` should be a matrix of pairwise distances or squared distances. :issue:`9775` by :user:`William de Vazelhes `. - \|API\| Deprecate ``precomputed`` parameter in function `manifold.t\_sne.trustworthiness`. Instead, the new parameter ``metric`` should be used with any compatible metric including 'precomputed', in which case the input matrix ``X`` should be a matrix of pairwise distances or squared distances. :issue:`9775` by :user:`William de Vazelhes `. :mod:`sklearn.metrics` ...................... - \|MajorFeature\| Added the :func:`metrics.davies\_bouldin\_score` metric for evaluation of clustering models without a ground truth. :issue:`10827` by :user:`Luis Osa `. - \|MajorFeature\| Added the :func:`metrics.balanced\_accuracy\_score` metric and a corresponding ``'balanced\_accuracy'`` scorer for binary and multiclass classification. :issue:`8066` by :user:`xyguo` and :user:`Aman Dalmia `, and :issue:`10587` by `Joel Nothman`\_. - \|Feature\| Partial AUC	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.1238350123167038, -0.06749863922595978, -0.11211873590946198, -0.011684853583574295, 0.07258497923612595, 0.021557506173849106, -0.03724166750907898, 0.023447157815098763, -0.07527825981378555, 0.04986961558461189, 0.07170745730400085, -0.02566845342516899, 0.017274728044867516, -0.0491...	-0.018898
Added the :func:`metrics.davies\_bouldin\_score` metric for evaluation of clustering models without a ground truth. :issue:`10827` by :user:`Luis Osa `. - \|MajorFeature\| Added the :func:`metrics.balanced\_accuracy\_score` metric and a corresponding ``'balanced\_accuracy'`` scorer for binary and multiclass classification. :issue:`8066` by :user:`xyguo` and :user:`Aman Dalmia `, and :issue:`10587` by `Joel Nothman`\_. - \|Feature\| Partial AUC is available via ``max\_fpr`` parameter in :func:`metrics.roc\_auc\_score`. :issue:`3840` by :user:`Alexander Niederbühl `. - \|Feature\| A scorer based on :func:`metrics.brier\_score\_loss` is also available. :issue:`9521` by :user:`Hanmin Qin `. - \|Feature\| Added control over the normalization in :func:`metrics.normalized\_mutual\_info\_score` and :func:`metrics.adjusted\_mutual\_info\_score` via the ``average\_method`` parameter. In version 0.22, the default normalizer for each will become the \arithmetic\ mean of the entropies of each clustering. :issue:`11124` by :user:`Arya McCarthy `. - \|Feature\| Added ``output\_dict`` parameter in :func:`metrics.classification\_report` to return classification statistics as dictionary. :issue:`11160` by :user:`Dan Barkhorn `. - \|Feature\| :func:`metrics.classification\_report` now reports all applicable averages on the given data, including micro, macro and weighted average as well as samples average for multilabel data. :issue:`11679` by :user:`Alexander Pacha `. - \|Feature\| :func:`metrics.average\_precision\_score` now supports binary ``y\_true`` other than ``{0, 1}`` or ``{-1, 1}`` through ``pos\_label`` parameter. :issue:`9980` by :user:`Hanmin Qin `. - \|Feature\| :func:`metrics.label\_ranking\_average\_precision\_score` now supports ``sample\_weight``. :issue:`10845` by :user:`Jose Perez-Parras Toledano `. - \|Feature\| Add ``dense\_output`` parameter to :func:`metrics.pairwise.linear\_kernel`. When False and both inputs are sparse, will return a sparse matrix. :issue:`10999` by :user:`Taylor G Smith `. - \|Efficiency\| :func:`metrics.silhouette\_score` and :func:`metrics.silhouette\_samples` are more memory efficient and run faster. This avoids some reported freezes and MemoryErrors. :issue:`11135` by `Joel Nothman`\_. - \|Fix\| Fixed a bug in :func:`metrics.precision\_recall\_fscore\_support` when truncated `range(n\_labels)` is passed as value for `labels`. :issue:`10377` by :user:`Gaurav Dhingra `. - \|Fix\| Fixed a bug due to floating point error in :func:`metrics.roc\_auc\_score` with non-integer sample weights. :issue:`9786` by :user:`Hanmin Qin `. - \|Fix\| Fixed a bug where :func:`metrics.roc\_curve` sometimes starts on y-axis instead of (0, 0), which is inconsistent with the document and other implementations. Note that this will not influence the result from :func:`metrics.roc\_auc\_score` :issue:`10093` by :user:`alexryndin ` and :user:`Hanmin Qin `. - \|Fix\| Fixed a bug to avoid integer overflow. Casted product to 64 bits integer in :func:`metrics.mutual\_info\_score`. :issue:`9772` by :user:`Kumar Ashutosh `. - \|Fix\| Fixed a bug where :func:`metrics.average\_precision\_score` will sometimes return ``nan`` when ``sample\_weight`` contains 0. :issue:`9980` by :user:`Hanmin Qin `. - \|Fix\| Fixed a bug in :func:`metrics.fowlkes\_mallows\_score` to avoid integer overflow. Casted return value of `contingency\_matrix` to `int64` and computed product of square roots rather than square root of product. :issue:`9515` by :user:`Alan Liddell ` and :user:`Manh Dao `. - \|API\| Deprecate ``reorder`` parameter in :func:`metrics.auc` as it's no longer required for :func:`metrics.roc\_auc\_score`. Moreover using ``reorder=True`` can hide bugs due to floating point error in the input. :issue:`9851` by :user:`Hanmin Qin `. - \|API\| In :func:`metrics.normalized\_mutual\_info\_score` and :func:`metrics.adjusted\_mutual\_info\_score`, warn that ``average\_method`` will have a new default value. In version 0.22, the default normalizer for each will become the \arithmetic\ mean of the entropies of each clustering. Currently, :func:`metrics.normalized\_mutual\_info\_score` uses the default of ``average\_method='geometric'``, and :func:`metrics.adjusted\_mutual\_info\_score` uses the default of ``average\_method='max'`` to match their behaviors in version 0.19. :issue:`11124` by :user:`Arya McCarthy `. - \|API\| The ``batch\_size`` parameter to :func:`metrics.pairwise\_distances\_argmin\_min` and :func:`metrics.pairwise\_distances\_argmin` is deprecated to be removed in v0.22. It no longer has any effect, as batch size is determined by global ``working\_memory`` config. See :ref:`working\_memory`. :issue:`10280` by `Joel Nothman`\_ and :user:`Aman Dalmia `. :mod:`sklearn.mixture` ...................... - \|Feature\| Added function :term:`fit\_predict` to :class:`mixture.GaussianMixture` and :class:`mixture.GaussianMixture`, which is essentially equivalent to calling :term:`fit` and :term:`predict`. :issue:`10336` by :user:`Shu Haoran ` and :user:`Andrew Peng `. - \|Fix\| Fixed a bug in `mixture.BaseMixture` where the reported `n\_iter\_` was missing an iteration. It affected :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture`. :issue:`10740` by :user:`Erich Schubert `	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.017132403329014778, -0.07322145998477936, -0.08037259429693222, 0.024218697100877762, 0.06123397871851921, -0.0036352851893752813, -0.0043454719707369804, 0.06698323786258698, -0.04878738150000572, 0.0071579646319150925, -0.010109296068549156, -0.16392219066619873, -0.013573464937508106, ...	0.133499
Added function :term:`fit\_predict` to :class:`mixture.GaussianMixture` and :class:`mixture.GaussianMixture`, which is essentially equivalent to calling :term:`fit` and :term:`predict`. :issue:`10336` by :user:`Shu Haoran ` and :user:`Andrew Peng `. - \|Fix\| Fixed a bug in `mixture.BaseMixture` where the reported `n\_iter\_` was missing an iteration. It affected :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture`. :issue:`10740` by :user:`Erich Schubert ` and :user:`Guillaume Lemaitre `. - \|Fix\| Fixed a bug in `mixture.BaseMixture` and its subclasses :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture` where the ``lower\_bound\_`` was not the max lower bound across all initializations (when ``n\_init > 1``), but just the lower bound of the last initialization. :issue:`10869` by :user:`Aurélien Géron `. :mod:`sklearn.model\_selection` .............................. - \|Feature\| Add `return\_estimator` parameter in :func:`model\_selection.cross\_validate` to return estimators fitted on each split. :issue:`9686` by :user:`Aurélien Bellet `. - \|Feature\| New ``refit\_time\_`` attribute will be stored in :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` if ``refit`` is set to ``True``. This will allow measuring the complete time it takes to perform hyperparameter optimization and refitting the best model on the whole dataset. :issue:`11310` by :user:`Matthias Feurer `. - \|Feature\| Expose `error\_score` parameter in :func:`model\_selection.cross\_validate`, :func:`model\_selection.cross\_val\_score`, :func:`model\_selection.learning\_curve` and :func:`model\_selection.validation\_curve` to control the behavior triggered when an error occurs in `model\_selection.\_fit\_and\_score`. :issue:`11576` by :user:`Samuel O. Ronsin `. - \|Feature\| `BaseSearchCV` now has an experimental, private interface to support customized parameter search strategies, through its ``\_run\_search`` method. See the implementations in :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` and please provide feedback if you use this. Note that we do not assure the stability of this API beyond version 0.20. :issue:`9599` by `Joel Nothman`\_ - \|Enhancement\| Add improved error message in :func:`model\_selection.cross\_val\_score` when multiple metrics are passed in ``scoring`` keyword. :issue:`11006` by :user:`Ming Li `. - \|API\| The default number of cross-validation folds ``cv`` and the default number of splits ``n\_splits`` in the :class:`model\_selection.KFold`-like splitters will change from 3 to 5 in 0.22 as 3-fold has a lot of variance. :issue:`11557` by :user:`Alexandre Boucaud `. - \|API\| The default of ``iid`` parameter of :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` will change from ``True`` to ``False`` in version 0.22 to correspond to the standard definition of cross-validation, and the parameter will be removed in version 0.24 altogether. This parameter is of greatest practical significance where the sizes of different test sets in cross-validation were very unequal, i.e. in group-based CV strategies. :issue:`9085` by :user:`Laurent Direr ` and `Andreas Müller`\_. - \|API\| The default value of the ``error\_score`` parameter in :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` will change to ``np.NaN`` in version 0.22. :issue:`10677` by :user:`Kirill Zhdanovich `. - \|API\| Changed ValueError exception raised in :class:`model\_selection.ParameterSampler` to a UserWarning for case where the class is instantiated with a greater value of ``n\_iter`` than the total space of parameters in the parameter grid. ``n\_iter`` now acts as an upper bound on iterations. :issue:`10982` by :user:`Juliet Lawton ` - \|API\| Invalid input for :class:`model\_selection.ParameterGrid` now raises TypeError. :issue:`10928` by :user:`Solutus Immensus ` :mod:`sklearn.multioutput` .......................... - \|MajorFeature\| Added :class:`multioutput.RegressorChain` for multi-target regression. :issue:`9257` by :user:`Kumar Ashutosh `. :mod:`sklearn.naive\_bayes` .......................... - \|MajorFeature\| Added :class:`naive\_bayes.ComplementNB`, which implements the Complement Naive Bayes classifier described in Rennie et al. (2003). :issue:`8190` by :user:`Michael A. Alcorn `. - \|Feature\| Add `var\_smoothing` parameter in :class:`naive\_bayes.GaussianNB` to give a precise control over variances calculation. :issue:`9681` by :user:`Dmitry Mottl `. - \|Fix\| Fixed a bug in :class:`naive\_bayes.GaussianNB` which incorrectly raised error for prior list which summed to 1. :issue:`10005` by :user:`Gaurav Dhingra `. - \|Fix\| Fixed a bug in :class:`naive\_bayes.MultinomialNB` which did not accept vector valued pseudocounts (alpha). :issue:`10346` by :user:`Tobias Madsen ` :mod:`sklearn.neighbors` ........................ - \|Efficiency\| :class:`neighbors.RadiusNeighborsRegressor` and :class:`neighbors.RadiusNeighborsClassifier` are now parallelized according to ``n\_jobs`` regardless of ``algorithm``. :issue:`10887` by :user:`Joël Billaud `. - \|Efficiency\| :mod:`sklearn.neighbors` query methods are now more memory efficient when ``algorithm='brute'``. :issue:`11136` by `Joel Nothman`\_	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.05226783826947212, -0.09290356189012527, 0.060197677463293076, 0.010150418616831303, 0.023577380925416946, -0.02007942646741867, 0.048338912427425385, 0.019480470567941666, -0.13570640981197357, -0.06753064692020416, 0.0403415746986866, -0.08555049449205399, 0.07546243071556091, -0.0601...	0.031016
which did not accept vector valued pseudocounts (alpha). :issue:`10346` by :user:`Tobias Madsen ` :mod:`sklearn.neighbors` ........................ - \|Efficiency\| :class:`neighbors.RadiusNeighborsRegressor` and :class:`neighbors.RadiusNeighborsClassifier` are now parallelized according to ``n\_jobs`` regardless of ``algorithm``. :issue:`10887` by :user:`Joël Billaud `. - \|Efficiency\| :mod:`sklearn.neighbors` query methods are now more memory efficient when ``algorithm='brute'``. :issue:`11136` by `Joel Nothman`\_ and :user:`Aman Dalmia `. - \|Feature\| Add ``sample\_weight`` parameter to the fit method of :class:`neighbors.KernelDensity` to enable weighting in kernel density estimation. :issue:`4394` by :user:`Samuel O. Ronsin `. - \|Feature\| Novelty detection with :class:`neighbors.LocalOutlierFactor`: Add a ``novelty`` parameter to :class:`neighbors.LocalOutlierFactor`. When ``novelty`` is set to True, :class:`neighbors.LocalOutlierFactor` can then be used for novelty detection, i.e. predict on new unseen data. Available prediction methods are ``predict``, ``decision\_function`` and ``score\_samples``. By default, ``novelty`` is set to ``False``, and only the ``fit\_predict`` method is available. By :user:`Albert Thomas `. - \|Fix\| Fixed a bug in :class:`neighbors.NearestNeighbors` where fitting a NearestNeighbors model fails when a) the distance metric used is a callable and b) the input to the NearestNeighbors model is sparse. :issue:`9579` by :user:`Thomas Kober `. - \|Fix\| Fixed a bug so ``predict`` in :class:`neighbors.RadiusNeighborsRegressor` can handle empty neighbor set when using non uniform weights. Also raises a new warning when no neighbors are found for samples. :issue:`9655` by :user:`Andreas Bjerre-Nielsen `. - \|Fix\| \|Efficiency\| Fixed a bug in ``KDTree`` construction that results in faster construction and querying times. :issue:`11556` by :user:`Jake VanderPlas ` - \|Fix\| Fixed a bug in :class:`neighbors.KDTree` and :class:`neighbors.BallTree` where pickled tree objects would change their type to the super class `BinaryTree`. :issue:`11774` by :user:`Nicolas Hug `. :mod:`sklearn.neural\_network` ............................. - \|Feature\| Add `n\_iter\_no\_change` parameter in `neural\_network.BaseMultilayerPerceptron`, :class:`neural\_network.MLPRegressor`, and :class:`neural\_network.MLPClassifier` to give control over maximum number of epochs to not meet ``tol`` improvement. :issue:`9456` by :user:`Nicholas Nadeau `. - \|Fix\| Fixed a bug in `neural\_network.BaseMultilayerPerceptron`, :class:`neural\_network.MLPRegressor`, and :class:`neural\_network.MLPClassifier` with new ``n\_iter\_no\_change`` parameter now at 10 from previously hardcoded 2. :issue:`9456` by :user:`Nicholas Nadeau `. - \|Fix\| Fixed a bug in :class:`neural\_network.MLPRegressor` where fitting quit unexpectedly early due to local minima or fluctuations. :issue:`9456` by :user:`Nicholas Nadeau ` :mod:`sklearn.pipeline` ....................... - \|Feature\| The ``predict`` method of :class:`pipeline.Pipeline` now passes keyword arguments on to the pipeline's last estimator, enabling the use of parameters such as ``return\_std`` in a pipeline with caution. :issue:`9304` by :user:`Breno Freitas `. - \|API\| :class:`pipeline.FeatureUnion` now supports ``'drop'`` as a transformer to drop features. :issue:`11144` by :user:`Thomas Fan `. :mod:`sklearn.preprocessing` ............................ - \|MajorFeature\| Expanded :class:`preprocessing.OneHotEncoder` to allow to encode categorical string features as a numeric array using a one-hot (or dummy) encoding scheme, and added :class:`preprocessing.OrdinalEncoder` to convert to ordinal integers. Those two classes now handle encoding of all feature types (also handles string-valued features) and derives the categories based on the unique values in the features instead of the maximum value in the features. :issue:`9151` and :issue:`10521` by :user:`Vighnesh Birodkar ` and `Joris Van den Bossche`\_. - \|MajorFeature\| Added :class:`preprocessing.KBinsDiscretizer` for turning continuous features into categorical or one-hot encoded features. :issue:`7668`, :issue:`9647`, :issue:`10195`, :issue:`10192`, :issue:`11272`, :issue:`11467` and :issue:`11505`. by :user:`Henry Lin `, `Hanmin Qin`\_, `Tom Dupre la Tour`\_ and :user:`Giovanni Giuseppe Costa `. - \|MajorFeature\| Added :class:`preprocessing.PowerTransformer`, which implements the Yeo-Johnson and Box-Cox power transformations. Power transformations try to find a set of feature-wise parametric transformations to approximately map data to a Gaussian distribution centered at zero and with unit variance. This is useful as a variance-stabilizing transformation in situations where normality and homoscedasticity are desirable. :issue:`10210` by :user:`Eric Chang ` and :user:`Maniteja Nandana `, and :issue:`11520` by :user:`Nicolas Hug `. - \|MajorFeature\| NaN values are ignored and handled in the following preprocessing methods: :class:`preprocessing.MaxAbsScaler`, :class:`preprocessing.MinMaxScaler`, :class:`preprocessing.RobustScaler`, :class:`preprocessing.StandardScaler`, :class:`preprocessing.PowerTransformer`, :class:`preprocessing.QuantileTransformer` classes and :func:`preprocessing.maxabs\_scale`, :func:`preprocessing.minmax\_scale`, :func:`preprocessing.robust\_scale`, :func:`preprocessing.scale`, :func:`preprocessing.power\_transform`,	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.05672569200396538, -0.04826884716749191, -0.1105174794793129, -0.013830936513841152, 0.0412973053753376, -0.062010157853364944, -0.04698597639799118, -0.04046414792537689, -0.10404738783836365, -0.00818428210914135, 0.025652196258306503, 0.005879177711904049, 0.049813997000455856, -0.05...	0.033951
transformation in situations where normality and homoscedasticity are desirable. :issue:`10210` by :user:`Eric Chang ` and :user:`Maniteja Nandana `, and :issue:`11520` by :user:`Nicolas Hug `. - \|MajorFeature\| NaN values are ignored and handled in the following preprocessing methods: :class:`preprocessing.MaxAbsScaler`, :class:`preprocessing.MinMaxScaler`, :class:`preprocessing.RobustScaler`, :class:`preprocessing.StandardScaler`, :class:`preprocessing.PowerTransformer`, :class:`preprocessing.QuantileTransformer` classes and :func:`preprocessing.maxabs\_scale`, :func:`preprocessing.minmax\_scale`, :func:`preprocessing.robust\_scale`, :func:`preprocessing.scale`, :func:`preprocessing.power\_transform`, :func:`preprocessing.quantile\_transform` functions respectively addressed in issues :issue:`11011`, :issue:`11005`, :issue:`11308`, :issue:`11206`, :issue:`11306`, and :issue:`10437`. By :user:`Lucija Gregov ` and :user:`Guillaume Lemaitre `. - \|Feature\| :class:`preprocessing.PolynomialFeatures` now supports sparse input. :issue:`10452` by :user:`Aman Dalmia ` and `Joel Nothman`\_. - \|Feature\| :class:`preprocessing.RobustScaler` and :func:`preprocessing.robust\_scale` can be fitted using sparse matrices. :issue:`11308` by :user:`Guillaume Lemaitre `. - \|Feature\| :class:`preprocessing.OneHotEncoder` now supports the `get\_feature\_names` method to obtain the transformed feature names. :issue:`10181` by :user:`Nirvan Anjirbag ` and `Joris Van den Bossche`\_. - \|Feature\| A parameter ``check\_inverse`` was added to :class:`preprocessing.FunctionTransformer` to ensure that ``func`` and ``inverse\_func`` are the inverse of each other. :issue:`9399` by :user:`Guillaume Lemaitre `. - \|Feature\| The ``transform`` method of :class:`sklearn.preprocessing.MultiLabelBinarizer` now ignores any unknown classes. A warning is raised stating the unknown classes classes found which are ignored. :issue:`10913` by :user:`Rodrigo Agundez `. - \|Fix\| Fixed bugs in :class:`preprocessing.LabelEncoder` which would sometimes throw errors when ``transform`` or ``inverse\_transform`` was called with empty arrays. :issue:`10458` by :user:`Mayur Kulkarni `. - \|Fix\| Fix ValueError in :class:`preprocessing.LabelEncoder` when using ``inverse\_transform`` on unseen labels. :issue:`9816` by :user:`Charlie Newey `. - \|Fix\| Fix bug in :class:`preprocessing.OneHotEncoder` which discarded the ``dtype`` when returning a sparse matrix output. :issue:`11042` by :user:`Daniel Morales `. - \|Fix\| Fix ``fit`` and ``partial\_fit`` in :class:`preprocessing.StandardScaler` in the rare case when ``with\_mean=False`` and `with\_std=False` which was crashing by calling ``fit`` more than once and giving inconsistent results for ``mean\_`` whether the input was a sparse or a dense matrix. ``mean\_`` will be set to ``None`` with both sparse and dense inputs. ``n\_samples\_seen\_`` will be also reported for both input types. :issue:`11235` by :user:`Guillaume Lemaitre `. - \|API\| Deprecate ``n\_values`` and ``categorical\_features`` parameters and ``active\_features\_``, ``feature\_indices\_`` and ``n\_values\_`` attributes of :class:`preprocessing.OneHotEncoder`. The ``n\_values`` parameter can be replaced with the new ``categories`` parameter, and the attributes with the new ``categories\_`` attribute. Selecting the categorical features with the ``categorical\_features`` parameter is now better supported using the :class:`compose.ColumnTransformer`. :issue:`10521` by `Joris Van den Bossche`\_. - \|API\| Deprecate `preprocessing.Imputer` and move the corresponding module to :class:`impute.SimpleImputer`. :issue:`9726` by :user:`Kumar Ashutosh `. - \|API\| The ``axis`` parameter that was in `preprocessing.Imputer` is no longer present in :class:`impute.SimpleImputer`. The behavior is equivalent to ``axis=0`` (impute along columns). Row-wise imputation can be performed with FunctionTransformer (e.g., ``FunctionTransformer(lambda X: SimpleImputer().fit\_transform(X.T).T)``). :issue:`10829` by :user:`Guillaume Lemaitre ` and :user:`Gilberto Olimpio `. - \|API\| The NaN marker for the missing values has been changed between the `preprocessing.Imputer` and the `impute.SimpleImputer`. ``missing\_values='NaN'`` should now be ``missing\_values=np.nan``. :issue:`11211` by :user:`Jeremie du Boisberranger `. - \|API\| In :class:`preprocessing.FunctionTransformer`, the default of ``validate`` will be from ``True`` to ``False`` in 0.22. :issue:`10655` by :user:`Guillaume Lemaitre `. :mod:`sklearn.svm` .................. - \|Fix\| Fixed a bug in :class:`svm.SVC` where when the argument ``kernel`` is unicode in Python2, the ``predict\_proba`` method was raising an unexpected TypeError given dense inputs. :issue:`10412` by :user:`Jiongyan Zhang `. - \|API\| Deprecate ``random\_state`` parameter in :class:`svm.OneClassSVM` as the underlying implementation is not random. :issue:`9497` by :user:`Albert Thomas `. - \|API\| The default value of ``gamma`` parameter of :class:`svm.SVC`, :class:`~svm.NuSVC`, :class:`~svm.SVR`, :class:`~svm.NuSVR`, :class:`~svm.OneClassSVM` will change from ``'auto'`` to ``'scale'`` in version 0.22 to account better for unscaled features. :issue:`8361` by :user:`Gaurav Dhingra ` and :user:`Ting Neo `. :mod:`sklearn.tree` ................... - \|Enhancement\| Although private (and hence not assured API stability), `tree.\_criterion.ClassificationCriterion` and `tree.\_criterion.RegressionCriterion` may now be cimported and extended. :issue:`10325` by :user:`Camil Staps `. - \|Fix\| Fixed a bug in	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.04293256998062134, 0.06433218717575073, -0.04427596181631088, -0.02290409430861473, -0.053213272243738174, -0.10664285719394684, -0.05825568363070488, 0.05317854881286621, -0.10568247735500336, 0.0024489136412739754, 0.12722067534923553, -0.10109810531139374, -0.014170168898999691, 0.00...	0.105558
in version 0.22 to account better for unscaled features. :issue:`8361` by :user:`Gaurav Dhingra ` and :user:`Ting Neo `. :mod:`sklearn.tree` ................... - \|Enhancement\| Although private (and hence not assured API stability), `tree.\_criterion.ClassificationCriterion` and `tree.\_criterion.RegressionCriterion` may now be cimported and extended. :issue:`10325` by :user:`Camil Staps `. - \|Fix\| Fixed a bug in `tree.BaseDecisionTree` with `splitter="best"` where split threshold could become infinite when values in X were near infinite. :issue:`10536` by :user:`Jonathan Ohayon `. - \|Fix\| Fixed a bug in `tree.MAE` to ensure sample weights are being used during the calculation of tree MAE impurity. Previous behaviour could cause suboptimal splits to be chosen since the impurity calculation considered all samples to be of equal weight importance. :issue:`11464` by :user:`John Stott `. :mod:`sklearn.utils` .................... - \|Feature\| :func:`utils.check\_array` and :func:`utils.check\_X\_y` now have ``accept\_large\_sparse`` to control whether scipy.sparse matrices with 64-bit indices should be rejected. :issue:`11327` by :user:`Karan Dhingra ` and `Joel Nothman`\_. - \|Efficiency\| \|Fix\| Avoid copying the data in :func:`utils.check\_array` when the input data is a memmap (and ``copy=False``). :issue:`10663` by :user:`Arthur Mensch ` and :user:`Loïc Estève `. - \|API\| :func:`utils.check\_array` yield a ``FutureWarning`` indicating that arrays of bytes/strings will be interpreted as decimal numbers beginning in version 0.22. :issue:`10229` by :user:`Ryan Lee ` Multiple modules ................ - \|Feature\| \|API\| More consistent outlier detection API: Add a ``score\_samples`` method in :class:`svm.OneClassSVM`, :class:`ensemble.IsolationForest`, :class:`neighbors.LocalOutlierFactor`, :class:`covariance.EllipticEnvelope`. It allows to access raw score functions from original papers. A new ``offset\_`` parameter allows to link ``score\_samples`` and ``decision\_function`` methods. The ``contamination`` parameter of :class:`ensemble.IsolationForest` and :class:`neighbors.LocalOutlierFactor` ``decision\_function`` methods is used to define this ``offset\_`` such that outliers (resp. inliers) have negative (resp. positive) ``decision\_function`` values. By default, ``contamination`` is kept unchanged to 0.1 for a deprecation period. In 0.22, it will be set to "auto", thus using method-specific score offsets. In :class:`covariance.EllipticEnvelope` ``decision\_function`` method, the ``raw\_values`` parameter is deprecated as the shifted Mahalanobis distance will be always returned in 0.22. :issue:`9015` by `Nicolas Goix`\_. - \|Feature\| \|API\| A ``behaviour`` parameter has been introduced in :class:`ensemble.IsolationForest` to ensure backward compatibility. In the old behaviour, the ``decision\_function`` is independent of the ``contamination`` parameter. A threshold attribute depending on the ``contamination`` parameter is thus used. In the new behaviour the ``decision\_function`` is dependent on the ``contamination`` parameter, in such a way that 0 becomes its natural threshold to detect outliers. Setting behaviour to "old" is deprecated and will not be possible in version 0.22. Beside, the behaviour parameter will be removed in 0.24. :issue:`11553` by `Nicolas Goix`\_. - \|API\| Added convergence warning to :class:`svm.LinearSVC` and :class:`linear\_model.LogisticRegression` when ``verbose`` is set to 0. :issue:`10881` by :user:`Alexandre Sevin `. - \|API\| Changed warning type from :class:`UserWarning` to :class:`exceptions.ConvergenceWarning` for failing convergence in `linear\_model.logistic\_regression\_path`, :class:`linear\_model.RANSACRegressor`, :func:`linear\_model.ridge\_regression`, :class:`gaussian\_process.GaussianProcessRegressor`, :class:`gaussian\_process.GaussianProcessClassifier`, :func:`decomposition.fastica`, :class:`cross\_decomposition.PLSCanonical`, :class:`cluster.AffinityPropagation`, and :class:`cluster.Birch`. :issue:`10306` by :user:`Jonathan Siebert `. Miscellaneous ............. - \|MajorFeature\| A new configuration parameter, ``working\_memory`` was added to control memory consumption limits in chunked operations, such as the new :func:`metrics.pairwise\_distances\_chunked`. See :ref:`working\_memory`. :issue:`10280` by `Joel Nothman`\_ and :user:`Aman Dalmia `. - \|Feature\| The version of :mod:`joblib` bundled with Scikit-learn is now 0.12. This uses a new default multiprocessing implementation, named `loky `\_. While this may incur some memory and communication overhead, it should provide greater cross-platform stability than relying on Python standard library multiprocessing. :issue:`11741` by the Joblib developers, especially :user:`Thomas Moreau ` and `Olivier Grisel`\_. - \|Feature\| An environment variable to use the site joblib instead of the vendored one was added (:ref:`environment\_variable`). The main API of joblib is now exposed in :mod:`sklearn.utils`. :issue:`11166` by `Gael Varoquaux`\_. - \|Feature\| Add almost complete PyPy 3 support. Known unsupported functionalities are :func:`datasets.load\_svmlight\_file`, :class:`feature\_extraction.FeatureHasher` and :class:`feature\_extraction.text.HashingVectorizer`. For running on PyPy, PyPy3-v5.10+, Numpy 1.14.0+,	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.07995840162038803, -0.02133352868258953, 0.05017577484250069, 0.013575175777077675, 0.09915848076343536, -0.105336993932724, -0.03879958763718605, 0.06657588481903076, -0.09421595185995102, 0.04045804962515831, 0.02857636660337448, -0.0349932424724102, -0.021161610260605812, -0.00453500...	0.036452
to use the site joblib instead of the vendored one was added (:ref:`environment\_variable`). The main API of joblib is now exposed in :mod:`sklearn.utils`. :issue:`11166` by `Gael Varoquaux`\_. - \|Feature\| Add almost complete PyPy 3 support. Known unsupported functionalities are :func:`datasets.load\_svmlight\_file`, :class:`feature\_extraction.FeatureHasher` and :class:`feature\_extraction.text.HashingVectorizer`. For running on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ are required. :issue:`11010` by :user:`Ronan Lamy ` and `Roman Yurchak`\_. - \|Feature\| A utility method :func:`sklearn.show\_versions` was added to print out information relevant for debugging. It includes the user system, the Python executable, the version of the main libraries and BLAS binding information. :issue:`11596` by :user:`Alexandre Boucaud ` - \|Fix\| Fixed a bug when setting parameters on meta-estimator, involving both a wrapped estimator and its parameter. :issue:`9999` by :user:`Marcus Voss ` and `Joel Nothman`\_. - \|Fix\| Fixed a bug where calling :func:`sklearn.base.clone` was not thread safe and could result in a "pop from empty list" error. :issue:`9569` by `Andreas Müller`\_. - \|API\| The default value of ``n\_jobs`` is changed from ``1`` to ``None`` in all related functions and classes. ``n\_jobs=None`` means ``unset``. It will generally be interpreted as ``n\_jobs=1``, unless the current ``joblib.Parallel`` backend context specifies otherwise (See :term:`Glossary ` for additional information). Note that this change happens immediately (i.e., without a deprecation cycle). :issue:`11741` by `Olivier Grisel`\_. - \|Fix\| Fixed a bug in validation helpers where passing a Dask DataFrame results in an error. :issue:`12462` by :user:`Zachariah Miller ` Changes to estimator checks --------------------------- These changes mostly affect library developers. - Checks for transformers now apply if the estimator implements :term:`transform`, regardless of whether it inherits from :class:`sklearn.base.TransformerMixin`. :issue:`10474` by `Joel Nothman`\_. - Classifiers are now checked for consistency between :term:`decision\_function` and categorical predictions. :issue:`10500` by :user:`Narine Kokhlikyan `. - Allow tests in :func:`utils.estimator\_checks.check\_estimator` to test functions that accept pairwise data. :issue:`9701` by :user:`Kyle Johnson ` - Allow :func:`utils.estimator\_checks.check\_estimator` to check that there is no private settings apart from parameters during estimator initialization. :issue:`9378` by :user:`Herilalaina Rakotoarison ` - The set of checks in :func:`utils.estimator\_checks.check\_estimator` now includes a ``check\_set\_params`` test which checks that ``set\_params`` is equivalent to passing parameters in ``\_\_init\_\_`` and warns if it encounters parameter validation. :issue:`7738` by :user:`Alvin Chiang ` - Add invariance tests for clustering metrics. :issue:`8102` by :user:`Ankita Sinha ` and :user:`Guillaume Lemaitre `. - Add ``check\_methods\_subset\_invariance`` to :func:`~utils.estimator\_checks.check\_estimator`, which checks that estimator methods are invariant if applied to a data subset. :issue:`10428` by :user:`Jonathan Ohayon ` - Add tests in :func:`utils.estimator\_checks.check\_estimator` to check that an estimator can handle read-only memmap input data. :issue:`10663` by :user:`Arthur Mensch ` and :user:`Loïc Estève `. - ``check\_sample\_weights\_pandas\_series`` now uses 8 rather than 6 samples to accommodate for the default number of clusters in :class:`cluster.KMeans`. :issue:`10933` by :user:`Johannes Hansen `. - Estimators are now checked for whether ``sample\_weight=None`` equates to ``sample\_weight=np.ones(...)``. :issue:`11558` by :user:`Sergul Aydore `. Code and Documentation Contributors ----------------------------------- Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.19, including: 211217613, Aarshay Jain, absolutelyNoWarranty, Adam Greenhall, Adam Kleczewski, Adam Richie-Halford, adelr, AdityaDaflapurkar, Adrin Jalali, Aidan Fitzgerald, aishgrt1, Akash Shivram, Alan Liddell, Alan Yee, Albert Thomas, Alexander Lenail, Alexander-N, Alexandre Boucaud, Alexandre Gramfort, Alexandre Sevin, Alex Egg, Alvaro Perez-Diaz, Amanda, Aman Dalmia, Andreas Bjerre-Nielsen, Andreas Mueller, Andrew Peng, Angus Williams, Aniruddha Dave, annaayzenshtat, Anthony Gitter, Antonio Quinonez, Anubhav Marwaha, Arik Pamnani, Arthur Ozga, Artiem K, Arunava, Arya McCarthy, Attractadore, Aurélien Bellet, Aurélien Geron, Ayush Gupta, Balakumaran Manoharan, Bangda Sun, Barry Hart, Bastian Venthur, Ben Lawson, Benn Roth, Breno Freitas, Brent Yi, brett koonce, Caio Oliveira, Camil Staps, cclauss, Chady Kamar, Charlie Brummitt, Charlie Newey, chris, Chris, Chris Catalfo, Chris Foster, Chris Holdgraf, Christian Braune, Christian Hirsch, Christian	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.08692879229784012, -0.02842363715171814, -0.027369994670152664, -0.034605223685503006, 0.09316153824329376, -0.09007979184389114, -0.05247195437550545, 0.007135601248592138, -0.11570023000240326, -0.06267044693231583, -0.014025544747710228, 0.012905311770737171, -0.04804983362555504, -0...	-0.018801
McCarthy, Attractadore, Aurélien Bellet, Aurélien Geron, Ayush Gupta, Balakumaran Manoharan, Bangda Sun, Barry Hart, Bastian Venthur, Ben Lawson, Benn Roth, Breno Freitas, Brent Yi, brett koonce, Caio Oliveira, Camil Staps, cclauss, Chady Kamar, Charlie Brummitt, Charlie Newey, chris, Chris, Chris Catalfo, Chris Foster, Chris Holdgraf, Christian Braune, Christian Hirsch, Christian Hogan, Christopher Jenness, Clement Joudet, cnx, cwitte, Dallas Card, Dan Barkhorn, Daniel, Daniel Ferreira, Daniel Gomez, Daniel Klevebring, Danielle Shwed, Daniel Mohns, Danil Baibak, Darius Morawiec, David Beach, David Burns, David Kirkby, David Nicholson, David Pickup, Derek, Didi Bar-Zev, diegodlh, Dillon Gardner, Dillon Niederhut, dilutedsauce, dlovell, Dmitry Mottl, Dmitry Petrov, Dor Cohen, Douglas Duhaime, Ekaterina Tuzova, Eric Chang, Eric Dean Sanchez, Erich Schubert, Eunji, Fang-Chieh Chou, FarahSaeed, felix, Félix Raimundo, fenx, filipj8, FrankHui, Franz Wompner, Freija Descamps, frsi, Gabriele Calvo, Gael Varoquaux, Gaurav Dhingra, Georgi Peev, Gil Forsyth, Giovanni Giuseppe Costa, gkevinyen5418, goncalo-rodrigues, Gryllos Prokopis, Guillaume Lemaitre, Guillaume "Vermeille" Sanchez, Gustavo De Mari Pereira, hakaa1, Hanmin Qin, Henry Lin, Hong, Honghe, Hossein Pourbozorg, Hristo, Hunan Rostomyan, iampat, Ivan PANICO, Jaewon Chung, Jake VanderPlas, jakirkham, James Bourbeau, James Malcolm, Jamie Cox, Jan Koch, Jan Margeta, Jan Schlüter, janvanrijn, Jason Wolosonovich, JC Liu, Jeb Bearer, jeremiedbb, Jimmy Wan, Jinkun Wang, Jiongyan Zhang, jjabl, jkleint, Joan Massich, Joël Billaud, Joel Nothman, Johannes Hansen, JohnStott, Jonatan Samoocha, Jonathan Ohayon, Jörg Döpfert, Joris Van den Bossche, Jose Perez-Parras Toledano, josephsalmon, jotasi, jschendel, Julian Kuhlmann, Julien Chaumond, julietcl, Justin Shenk, Karl F, Kasper Primdal Lauritzen, Katrin Leinweber, Kirill, ksemb, Kuai Yu, Kumar Ashutosh, Kyeongpil Kang, Kye Taylor, kyledrogo, Leland McInnes, Léo DS, Liam Geron, Liutong Zhou, Lizao Li, lkjcalc, Loic Esteve, louib, Luciano Viola, Lucija Gregov, Luis Osa, Luis Pedro Coelho, Luke M Craig, Luke Persola, Mabel, Mabel Villalba, Maniteja Nandana, MarkIwanchyshyn, Mark Roth, Markus Müller, MarsGuy, Martin Gubri, martin-hahn, martin-kokos, mathurinm, Matthias Feurer, Max Copeland, Mayur Kulkarni, Meghann Agarwal, Melanie Goetz, Michael A. Alcorn, Minghui Liu, Ming Li, Minh Le, Mohamed Ali Jamaoui, Mohamed Maskani, Mohammad Shahebaz, Muayyad Alsadi, Nabarun Pal, Nagarjuna Kumar, Naoya Kanai, Narendran Santhanam, NarineK, Nathaniel Saul, Nathan Suh, Nicholas Nadeau, P.Eng., AVS, Nick Hoh, Nicolas Goix, Nicolas Hug, Nicolau Werneck, nielsenmarkus11, Nihar Sheth, Nikita Titov, Nilesh Kevlani, Nirvan Anjirbag, notmatthancock, nzw, Oleksandr Pavlyk, oliblum90, Oliver Rausch, Olivier Grisel, Oren Milman, Osaid Rehman Nasir, pasbi, Patrick Fernandes, Patrick Olden, Paul Paczuski, Pedro Morales, Peter, Peter St. John, pierreablin, pietruh, Pinaki Nath Chowdhury, Piotr Szymański, Pradeep Reddy Raamana, Pravar D Mahajan, pravarmahajan, QingYing Chen, Raghav RV, Rajendra arora, RAKOTOARISON Herilalaina, Rameshwar Bhaskaran, RankyLau, Rasul Kerimov, Reiichiro Nakano, Rob, Roman Kosobrodov, Roman Yurchak, Ronan Lamy, rragundez, Rüdiger Busche, Ryan, Sachin Kelkar, Sagnik Bhattacharya, Sailesh Choyal, Sam Radhakrishnan, Sam Steingold, Samuel Bell, Samuel O. Ronsin, Saqib Nizam Shamsi, SATISH J, Saurabh Gupta, Scott Gigante, Sebastian Flennerhag, Sebastian Raschka, Sebastien Dubois, Sébastien Lerique, Sebastin Santy, Sergey Feldman, Sergey Melderis, Sergul Aydore, Shahebaz, Shalil Awaley, Shangwu Yao, Sharad Vijalapuram, Sharan Yalburgi, shenhanc78, Shivam Rastogi, Shu Haoran, siftikha, Sinclert Pérez, SolutusImmensus, Somya Anand, srajan paliwal, Sriharsha Hatwar, Sri Krishna, Stefan van der Walt, Stephen McDowell, Steven Brown, syonekura, Taehoon Lee, Takanori Hayashi, tarcusx, Taylor G Smith, theriley106, Thomas, Thomas Fan, Thomas Heavey, Tobias Madsen, tobycheese, Tom Augspurger, Tom Dupré la Tour, Tommy, Trevor Stephens, Trishnendu Ghorai, Tulio Casagrande, twosigmajab, Umar Farouk Umar, Urvang Patel, Utkarsh Upadhyay, Vadim Markovtsev, Varun Agrawal, Vathsala Achar, Vilhelm von Ehrenheim, Vinayak Mehta, Vinit, Vinod Kumar L, Viraj Mavani, Viraj Navkal, Vivek Kumar, Vlad Niculae, vqean3, Vrishank Bhardwaj, vufg, wallygauze, Warut Vijitbenjaronk, wdevazelhes, Wenhao Zhang, Wes Barnett, Will, William de Vazelhes, Will Rosenfeld, Xin Xiong, Yiming (Paul) Li, ymazari, Yufeng, Zach Griffith, Zé Vinícius, Zhenqing Hu, Zhiqing Xiao, Zijie (ZJ) Poh	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.024448547512292862, -0.03349172696471214, -0.04019515961408615, 0.008325053378939629, 0.059117306023836136, 0.06344552338123322, -0.03559465706348419, 0.02010735124349594, 0.029292626306414604, -0.014657608233392239, -0.024452021345496178, -0.040881477296352386, -0.0019848074298352003, ...	0.021426
Vinod Kumar L, Viraj Mavani, Viraj Navkal, Vivek Kumar, Vlad Niculae, vqean3, Vrishank Bhardwaj, vufg, wallygauze, Warut Vijitbenjaronk, wdevazelhes, Wenhao Zhang, Wes Barnett, Will, William de Vazelhes, Will Rosenfeld, Xin Xiong, Yiming (Paul) Li, ymazari, Yufeng, Zach Griffith, Zé Vinícius, Zhenqing Hu, Zhiqing Xiao, Zijie (ZJ) Poh	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.20.rst	main	scikit-learn	[ -0.0930231511592865, -0.011860020458698273, -0.04885845258831978, -0.03459230437874794, -0.010928623378276825, 0.10465291887521744, 0.030852580443024635, 0.06005501747131348, -0.04687634855508804, 0.0148871885612607, 0.014473185874521732, -0.07064563035964966, 0.007527216803282499, 0.06247...	0.069541
.. include:: \_contributors.rst .. currentmodule:: sklearn ============ Version 0.21 ============ .. include:: changelog\_legend.inc .. \_changes\_0\_21\_3: Version 0.21.3 ============== \\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 :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.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 `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:`tree.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 sufficiently 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 `. `sklearn.utils.sparsefuncs` ........................... - \|Fix\| Fixed a bug where `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	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.045129746198654175, -0.06092588230967522, 0.047523029148578644, 0.011699172668159008, 0.10856600105762482, -0.00962087046355009, -0.04521416872739792, 0.0014118296094238758, -0.007734120357781649, 0.021968545392155647, 0.08613715320825577, -0.011384911835193634, -0.008035233244299889, -...	-0.007661
- \|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 `. `sklearn.utils.sparsefuncs` ........................... - \|Fix\| Fixed a bug where `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 `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`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.03967343643307686, -0.00610357103869319, -0.04823089763522148, -0.047103505581617355, -0.01330327894538641, -0.10302374511957169, -0.028792954981327057, 0.020784860476851463, -0.11187613010406494, 0.02288021333515644, 0.07591813057661057, -0.031519897282123566, -0.01378032099455595, -0....	0.05336
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 `\_	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.025413878262043, -0.05329195410013199, -0.11319345235824585, -0.045652102679014206, -0.03988756984472275, -0.05003930628299713, -0.04065540432929993, 0.007171378005295992, -0.04305924102663994, -0.0055802506394684315, -0.0006728109437972307, -0.07838061451911926, 0.039408326148986816, -...	0.045617
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 change 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 `ensemble.gradient\_boosting.LossFunction` and `ensemble.gradient\_boosting.LeastSquaresError` where the default value of ``learning\_rate``	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.13030189275741577, -0.08591367304325104, -0.040290214121341705, 0.0700099840760231, 0.11566992104053497, -0.04914599284529686, -0.052491433918476105, 0.033128172159194946, -0.059066031128168106, -0.02820284105837345, -0.018937814980745316, -0.036432940512895584, 0.03022773750126362, -0....	0.029214
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 `ensemble.gradient\_boosting.LossFunction` and `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\| `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\| `ensemble.partial\_dependence` and `ensemble.plot\_partial\_dependence` are now deprecated in favor of :func:`inspection.partial\_dependence` and `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 `. `sklearn.externals` ................... - \|API\| Deprecated `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 and `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 (`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\| `linear\_model.make\_dataset` now preserves ``float32`` and ``float64`` dtypes, reducing memory consumption in stochastic gradient, SAG and	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.08851532638072968, -0.05371485650539398, 0.02959536202251911, 0.04159381240606308, 0.07486674934625626, -0.015878938138484955, -0.04310183972120285, 0.029926665127277374, -0.028952086344361305, 0.038430050015449524, -0.015057948417961597, -0.02220246195793152, 0.01107463613152504, -0.11...	-0.01151
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\| `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 tolerance. :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 coefficients 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 `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\| `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.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`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.04739059507846832, -0.06182532384991646, -0.11409611999988556, 0.06029088422656059, 0.031415730714797974, -0.0636376142501831, 0.02029648795723915, 0.03527451679110527, -0.05933886393904686, 0.030463630333542824, 0.059885330498218536, -0.04206334799528122, 0.013008388690650463, -0.01062...	0.040078
- \|Efficiency\| Make :func:`manifold.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\| `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 `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	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.03698062151670456, -0.07269914448261261, -0.08083454519510269, 0.012774967588484287, 0.009833711199462414, -0.008710432797670364, 0.017950747162103653, 0.057045817375183105, -0.009014695882797241, 0.00966609362512827, 0.013253476470708847, -0.04380225017666817, 0.05607854202389717, 0.00...	0.089315
`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	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.07455135881900787, -0.046796344220638275, -0.04445585235953331, 0.0643317699432373, 0.049829814583063126, 0.008861199952661991, 0.010966965928673744, 0.028068436309695244, -0.06567635387182236, -0.047916777431964874, 0.03404613584280014, -0.10744551569223404, 0.010772611014544964, -0.05...	0.013795
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:`sklearn.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 `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 `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 `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 `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 `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.	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.11310965567827225, 0.005660108290612698, -0.0101292934268713, -0.04339797794818878, -0.07836338877677917, -0.10153653472661972, -0.05631257966160774, 0.038361914455890656, -0.009957786649465561, 0.02773178741335869, 0.04912879690527916, -0.07180583477020264, -0.01987745612859726, -0.066...	0.043428
` 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 `. .. rubric:: 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, ^\_\_^	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.21.rst	main	scikit-learn	[ -0.11420229077339172, -0.021646730601787567, -0.05740884691476822, 0.019399918615818024, 0.12073755264282227, -0.09565432369709015, -0.008697018027305603, -0.0014074729988351464, -0.023889973759651184, -0.04262108355760574, 0.02184641733765602, -0.03395439684391022, -0.030848730355501175, ...	0.137847
.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_2: =========== Version 1.2 =========== For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_1\_2\_0.py`. .. include:: changelog\_legend.inc .. \_changes\_1\_2\_2: Version 1.2.2 ============= \\March 2023\\ Changelog --------- :mod:`sklearn.base` ................... - \|Fix\| When `set\_output(transform="pandas")`, :class:`base.TransformerMixin` maintains the index if the :term:`transform` output is already a DataFrame. :pr:`25747` by `Thomas Fan`\_. :mod:`sklearn.calibration` .......................... - \|Fix\| A deprecation warning is raised when using the `base\_estimator\_\_` prefix to set parameters of the estimator used in :class:`calibration.CalibratedClassifierCV`. :pr:`25477` by :user:`Tim Head `. :mod:`sklearn.cluster` ...................... - \|Fix\| Fixed a bug in :class:`cluster.BisectingKMeans`, preventing `fit` from randomly failing due to a permutation of the labels when running multiple inits. :pr:`25563` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.compose` ...................... - \|Fix\| Fixes a bug in :class:`compose.ColumnTransformer` which now supports empty selection of columns when `set\_output(transform="pandas")`. :pr:`25570` by `Thomas Fan`\_. :mod:`sklearn.ensemble` ....................... - \|Fix\| A deprecation warning is raised when using the `base\_estimator\_\_` prefix to set parameters of the estimator used in :class:`ensemble.AdaBoostClassifier`, :class:`ensemble.AdaBoostRegressor`, :class:`ensemble.BaggingClassifier`, and :class:`ensemble.BaggingRegressor`. :pr:`25477` by :user:`Tim Head `. :mod:`sklearn.feature\_selection` ................................ - \|Fix\| Fixed a regression where a negative `tol` would not be accepted any more by :class:`feature\_selection.SequentialFeatureSelector`. :pr:`25664` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.inspection` ......................... - \|Fix\| Raise a more informative error message in :func:`inspection.partial\_dependence` when dealing with mixed data type categories that cannot be sorted by :func:`numpy.unique`. This problem usually happens when categories are `str` and missing values are present using `np.nan`. :pr:`25774` by :user:`Guillaume Lemaitre `. :mod:`sklearn.isotonic` ....................... - \|Fix\| Fixes a bug in :class:`isotonic.IsotonicRegression` where :meth:`isotonic.IsotonicRegression.predict` would return a pandas DataFrame when the global configuration sets `transform\_output="pandas"`. :pr:`25500` by :user:`Guillaume Lemaitre `. :mod:`sklearn.preprocessing` ............................ - \|Fix\| `preprocessing.OneHotEncoder.drop\_idx\_` now properly references the dropped category in the `categories\_` attribute when there are infrequent categories. :pr:`25589` by `Thomas Fan`\_. - \|Fix\| :class:`preprocessing.OrdinalEncoder` now correctly supports `encoded\_missing\_value` or `unknown\_value` set to a categories' cardinality when there is missing values in the training data. :pr:`25704` by `Thomas Fan`\_. :mod:`sklearn.tree` ................... - \|Fix\| Fixed a regression in :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier` and :class:`tree.ExtraTreeRegressor` where an error was no longer raised in version 1.2 when `min\_sample\_split=1`. :pr:`25744` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.utils` .................... - \|Fix\| Fixes a bug in :func:`utils.check\_array` which now correctly performs non-finite validation with the Array API specification. :pr:`25619` by `Thomas Fan`\_. - \|Fix\| :func:`utils.multiclass.type\_of\_target` can identify pandas nullable data types as classification targets. :pr:`25638` by `Thomas Fan`\_. .. \_changes\_1\_2\_1: Version 1.2.1 ============= \\January 2023\\ 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. - \|Fix\| The fitted components in :class:`decomposition.MiniBatchDictionaryLearning` might differ. The online updates of the sufficient statistics now properly take the sizes of the batches into account. :pr:`25354` by :user:`Jérémie du Boisberranger `. - \|Fix\| The `categories\_` attribute of :class:`preprocessing.OneHotEncoder` now always contains an array of `object`s when using predefined categories that are strings. Predefined categories encoded as bytes will no longer work with `X` encoded as strings. :pr:`25174` by :user:`Tim Head `. Changes impacting all modules ----------------------------- - \|Fix\| Support `pandas.Int64` dtyped `y` for classifiers and regressors. :pr:`25089` by :user:`Tim Head `. - \|Fix\| Remove spurious warnings for estimators internally using neighbors search methods. :pr:`25129` by :user:`Julien Jerphanion `. - \|Fix\| Fix a bug where the current configuration was ignored in estimators using `n\_jobs > 1`. This bug was triggered for tasks dispatched by the auxiliary thread of `joblib` as :func:`sklearn.get\_config` used to access an empty thread local configuration instead of the configuration visible from the thread	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.050932951271533966, -0.025926509872078896, 0.03204238787293434, -0.044852450489997864, 0.12385556101799011, -0.00037770901690237224, 0.010805705562233925, -0.0013735295506194234, -0.015092344954609871, -0.012877280823886395, 0.09375476092100143, -0.0005094001535326242, -0.0557493492960929...	0.072065
Jerphanion `. - \|Fix\| Fix a bug where the current configuration was ignored in estimators using `n\_jobs > 1`. This bug was triggered for tasks dispatched by the auxiliary thread of `joblib` as :func:`sklearn.get\_config` used to access an empty thread local configuration instead of the configuration visible from the thread where `joblib.Parallel` was first called. :pr:`25363` by :user:`Guillaume Lemaitre `. Changelog --------- :mod:`sklearn.base` ................... - \|Fix\| Fix a regression in `BaseEstimator.\_\_getstate\_\_` that would prevent certain estimators from being pickled when using Python 3.11. :pr:`25188` by :user:`Benjamin Bossan `. - \|Fix\| Inheriting from :class:`base.TransformerMixin` will only wrap the `transform` method if the class defines `transform` itself. :pr:`25295` by `Thomas Fan`\_. :mod:`sklearn.datasets` ....................... - \|Fix\| Fixes an inconsistency in :func:`datasets.fetch\_openml` between liac-arff and pandas parser when a leading space is introduced after the delimiter. The ARFF specs require ignoring the leading space. :pr:`25312` by :user:`Guillaume Lemaitre `. - \|Fix\| Fixes a bug in :func:`datasets.fetch\_openml` when using `parser="pandas"` where single quote and backslash escape characters were not properly handled. :pr:`25511` by :user:`Guillaume Lemaitre `. :mod:`sklearn.decomposition` ............................ - \|Fix\| Fixed a bug in :class:`decomposition.MiniBatchDictionaryLearning` where the online updates of the sufficient statistics were not correct when calling `partial\_fit` on batches of different sizes. :pr:`25354` by :user:`Jérémie du Boisberranger `. - \|Fix\| :class:`decomposition.DictionaryLearning` better supports readonly NumPy arrays. In particular, it better supports large datasets which are memory-mapped when it is used with coordinate descent algorithms (i.e. when `fit\_algorithm='cd'`). :pr:`25172` by :user:`Julien Jerphanion `. :mod:`sklearn.ensemble` ....................... - \|Fix\| :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier` and :class:`ensemble.ExtraTreesRegressor` now support sparse readonly datasets. :pr:`25341` by :user:`Julien Jerphanion ` :mod:`sklearn.feature\_extraction` ................................. - \|Fix\| :class:`feature\_extraction.FeatureHasher` raises an informative error when the input is a list of strings. :pr:`25094` by `Thomas Fan`\_. :mod:`sklearn.linear\_model` ........................... - \|Fix\| Fix a regression in :class:`linear\_model.SGDClassifier` and :class:`linear\_model.SGDRegressor` that makes them unusable with the `verbose` parameter set to a value greater than 0. :pr:`25250` by :user:`Jérémie Du Boisberranger `. :mod:`sklearn.manifold` ....................... - \|Fix\| :class:`manifold.TSNE` now works correctly when output type is set to pandas :pr:`25370` by :user:`Tim Head `. :mod:`sklearn.model\_selection` .............................. - \|Fix\| :func:`model\_selection.cross\_validate` with multimetric scoring in case of some failing scorers the non-failing scorers now return proper scores instead of `error\_score` values. :pr:`23101` by :user:`András Simon ` and `Thomas Fan`\_. :mod:`sklearn.neural\_network` ............................. - \|Fix\| :class:`neural\_network.MLPClassifier` and :class:`neural\_network.MLPRegressor` no longer raise warnings when fitting data with feature names. :pr:`24873` by :user:`Tim Head `. - \|Fix\| Improves error message in :class:`neural\_network.MLPClassifier` and :class:`neural\_network.MLPRegressor`, when `early\_stopping=True` and `partial\_fit` is called. :pr:`25694` by `Thomas Fan`\_. :mod:`sklearn.preprocessing` ............................ - \|Fix\| :meth:`preprocessing.FunctionTransformer.inverse\_transform` correctly supports DataFrames that are all numerical when `check\_inverse=True`. :pr:`25274` by `Thomas Fan`\_. - \|Fix\| :meth:`preprocessing.SplineTransformer.get\_feature\_names\_out` correctly returns feature names when `extrapolations="periodic"`. :pr:`25296` by `Thomas Fan`\_. :mod:`sklearn.tree` ................... - \|Fix\| :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor` :class:`tree.ExtraTreeClassifier` and :class:`tree.ExtraTreeRegressor` now support sparse readonly datasets. :pr:`25341` by :user:`Julien Jerphanion ` :mod:`sklearn.utils` .................... - \|Fix\| Restore :func:`utils.check\_array`'s behaviour for pandas Series of type boolean. The type is maintained, instead of converting to `float64.` :pr:`25147` by :user:`Tim Head `. - \|API\| `utils.fixes.delayed` is deprecated in 1.2.1 and will be removed in 1.5. Instead, import :func:`utils.parallel.delayed` and use it in conjunction with the newly introduced :func:`utils.parallel.Parallel` to ensure proper propagation of the scikit-learn configuration to the workers. :pr:`25363` by :user:`Guillaume Lemaitre `. .. \_changes\_1\_2: Version 1.2.0 ============= \\December 2022\\ 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. - \|Enhancement\| The default `eigen\_tol` for :class:`cluster.SpectralClustering`, :class:`manifold.SpectralEmbedding`, :func:`cluster.spectral\_clustering`, and :func:`manifold.spectral\_embedding` is now `None` when using the `'amg'` or `'lobpcg'` solvers. This change improves numerical stability	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.17395922541618347, -0.015526351518929005, -0.013588321395218372, 0.009959685616195202, 0.052411433309316635, -0.08842634409666061, -0.03332017362117767, -0.027813879773020744, -0.04885386303067207, -0.029052797704935074, 0.038003623485565186, -0.005958921741694212, 0.003178122453391552, ...	0.107408
models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures. - \|Enhancement\| The default `eigen\_tol` for :class:`cluster.SpectralClustering`, :class:`manifold.SpectralEmbedding`, :func:`cluster.spectral\_clustering`, and :func:`manifold.spectral\_embedding` is now `None` when using the `'amg'` or `'lobpcg'` solvers. This change improves numerical stability of the solver, but may result in a different model. - \|Enhancement\| :class:`linear\_model.GammaRegressor`, :class:`linear\_model.PoissonRegressor` and :class:`linear\_model.TweedieRegressor` can reach higher precision with the lbfgs solver, in particular when `tol` is set to a tiny value. Moreover, `verbose` is now properly propagated to L-BFGS-B. :pr:`23619` by :user:`Christian Lorentzen `. - \|Enhancement\| The default value for `eps` :func:`metrics.log\_loss` has changed from `1e-15` to `"auto"`. `"auto"` sets `eps` to `np.finfo(y\_pred.dtype).eps`. :pr:`24354` by :user:`Safiuddin Khaja ` and :user:`gsiisg `. - \|Fix\| Make sign of `components\_` deterministic in :class:`decomposition.SparsePCA`. :pr:`23935` by :user:`Guillaume Lemaitre `. - \|Fix\| The `components\_` signs in :class:`decomposition.FastICA` might differ. It is now consistent and deterministic with all SVD solvers. :pr:`22527` by :user:`Meekail Zain ` and `Thomas Fan`\_. - \|Fix\| The condition for early stopping has now been changed in `linear\_model.\_sgd\_fast.\_plain\_sgd` which is used by :class:`linear\_model.SGDRegressor` and :class:`linear\_model.SGDClassifier`. The old condition did not disambiguate between training and validation set and had an effect of overscaling the error tolerance. This has been fixed in :pr:`23798` by :user:`Harsh Agrawal `. - \|Fix\| For :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` ranks corresponding to nan scores will all be set to the maximum possible rank. :pr:`24543` by :user:`Guillaume Lemaitre `. - \|API\| The default value of `tol` was changed from `1e-3` to `1e-4` for :func:`linear\_model.ridge\_regression`, :class:`linear\_model.Ridge` and :class:`linear\_model.RidgeClassifier`. :pr:`24465` by :user:`Christian Lorentzen `. Changes impacting all modules ----------------------------- - \|MajorFeature\| The `set\_output` API has been adopted by all transformers. Meta-estimators that contain transformers such as :class:`pipeline.Pipeline` or :class:`compose.ColumnTransformer` also define a `set\_output`. For details, see `SLEP018 `\_\_. :pr:`23734` and :pr:`24699` by `Thomas Fan`\_. - \|Efficiency\| Low-level routines for reductions on pairwise distances for dense float32 datasets have been refactored. The following functions and estimators now benefit from improved performances in terms of hardware scalability and speed-ups: - :func:`sklearn.metrics.pairwise\_distances\_argmin` - :func:`sklearn.metrics.pairwise\_distances\_argmin\_min` - :class:`sklearn.cluster.AffinityPropagation` - :class:`sklearn.cluster.Birch` - :class:`sklearn.cluster.MeanShift` - :class:`sklearn.cluster.OPTICS` - :class:`sklearn.cluster.SpectralClustering` - :func:`sklearn.feature\_selection.mutual\_info\_regression` - :class:`sklearn.neighbors.KNeighborsClassifier` - :class:`sklearn.neighbors.KNeighborsRegressor` - :class:`sklearn.neighbors.RadiusNeighborsClassifier` - :class:`sklearn.neighbors.RadiusNeighborsRegressor` - :class:`sklearn.neighbors.LocalOutlierFactor` - :class:`sklearn.neighbors.NearestNeighbors` - :class:`sklearn.manifold.Isomap` - :class:`sklearn.manifold.LocallyLinearEmbedding` - :class:`sklearn.manifold.TSNE` - :func:`sklearn.manifold.trustworthiness` - :class:`sklearn.semi\_supervised.LabelPropagation` - :class:`sklearn.semi\_supervised.LabelSpreading` For instance :meth:`sklearn.neighbors.NearestNeighbors.kneighbors` and :meth:`sklearn.neighbors.NearestNeighbors.radius\_neighbors` can respectively be up to ×20 and ×5 faster than previously on a laptop. Moreover, implementations of those two algorithms are now suitable for machine with many cores, making them usable for datasets consisting of millions of samples. :pr:`23865` by :user:`Julien Jerphanion `. - \|Enhancement\| Finiteness checks (detection of NaN and infinite values) in all estimators are now significantly more efficient for float32 data by leveraging NumPy's SIMD optimized primitives. :pr:`23446` by :user:`Meekail Zain ` - \|Enhancement\| Finiteness checks (detection of NaN and infinite values) in all estimators are now faster by utilizing a more efficient stop-on-first second-pass algorithm. :pr:`23197` by :user:`Meekail Zain ` - \|Enhancement\| Support for combinations of dense and sparse datasets pairs for all distance metrics and for float32 and float64 datasets has been added or has seen its performance improved for the following estimators: - :func:`sklearn.metrics.pairwise\_distances\_argmin` - :func:`sklearn.metrics.pairwise\_distances\_argmin\_min` - :class:`sklearn.cluster.AffinityPropagation` - :class:`sklearn.cluster.Birch` - :class:`sklearn.cluster.SpectralClustering` - :class:`sklearn.neighbors.KNeighborsClassifier` - :class:`sklearn.neighbors.KNeighborsRegressor` - :class:`sklearn.neighbors.RadiusNeighborsClassifier` - :class:`sklearn.neighbors.RadiusNeighborsRegressor` - :class:`sklearn.neighbors.LocalOutlierFactor` - :class:`sklearn.neighbors.NearestNeighbors` - :class:`sklearn.manifold.Isomap` - :class:`sklearn.manifold.TSNE` - :func:`sklearn.manifold.trustworthiness` :pr:`23604` and :pr:`23585` by :user:`Julien Jerphanion `, :user:`Olivier Grisel `, and `Thomas Fan`\_, :pr:`24556` by :user:`Vincent Maladière `. - \|Fix\| Systematically check the sha256 digest of dataset tarballs used in code examples in the documentation. :pr:`24617` by :user:`Olivier Grisel ` and `Thomas Fan`\_. Thanks to `Sim4n6 `\_ for the report. Changelog --------- ..	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.05905270203948021, -0.10434664040803909, 0.015025179833173752, 0.05816803500056267, 0.01394193060696125, -0.047341350466012955, -0.07327425479888916, 0.005593771114945412, -0.02507317066192627, 0.010626700706779957, 0.04554947093129158, -0.020722702145576477, -0.0033847279846668243, -0....	-0.11188
by :user:`Julien Jerphanion `, :user:`Olivier Grisel `, and `Thomas Fan`\_, :pr:`24556` by :user:`Vincent Maladière `. - \|Fix\| Systematically check the sha256 digest of dataset tarballs used in code examples in the documentation. :pr:`24617` by :user:`Olivier Grisel ` and `Thomas Fan`\_. Thanks to `Sim4n6 `\_ for the report. Changelog --------- .. 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` ................... - \|Enhancement\| Introduces :class:`base.ClassNamePrefixFeaturesOutMixin` and :class:`base.ClassNamePrefixFeaturesOutMixin` mixins that define :term:`get\_feature\_names\_out` for common transformer use cases. :pr:`24688` by `Thomas Fan`\_. :mod:`sklearn.calibration` .......................... - \|API\| Rename `base\_estimator` to `estimator` in :class:`calibration.CalibratedClassifierCV` to improve readability and consistency. The parameter `base\_estimator` is deprecated and will be removed in 1.4. :pr:`22054` by :user:`Kevin Roice `. :mod:`sklearn.cluster` ...................... - \|Efficiency\| :class:`cluster.KMeans` with `algorithm="lloyd"` is now faster and uses less memory. :pr:`24264` by :user:`Vincent Maladiere `. - \|Enhancement\| The `predict` and `fit\_predict` methods of :class:`cluster.OPTICS` now accept sparse data type for input data. :pr:`14736` by :user:`Hunt Zhan `, :pr:`20802` by :user:`Brandon Pokorny `, and :pr:`22965` by :user:`Meekail Zain `. - \|Enhancement\| :class:`cluster.Birch` now preserves dtype for `numpy.float32` inputs. :pr:`22968` by `Meekail Zain `. - \|Enhancement\| :class:`cluster.KMeans` and :class:`cluster.MiniBatchKMeans` now accept a new `'auto'` option for `n\_init` which changes the number of random initializations to one when using `init='k-means++'` for efficiency. This begins deprecation for the default values of `n\_init` in the two classes and both will have their defaults changed to `n\_init='auto'` in 1.4. :pr:`23038` by :user:`Meekail Zain `. - \|Enhancement\| :class:`cluster.SpectralClustering` and :func:`cluster.spectral\_clustering` now propagate the `eigen\_tol` parameter to all choices of `eigen\_solver`. Includes a new option `eigen\_tol="auto"` and begins deprecation to change the default from `eigen\_tol=0` to `eigen\_tol="auto"` in version 1.3. :pr:`23210` by :user:`Meekail Zain `. - \|Fix\| :class:`cluster.KMeans` now supports readonly attributes when predicting. :pr:`24258` by `Thomas Fan`\_ - \|API\| The `affinity` attribute is now deprecated for :class:`cluster.AgglomerativeClustering` and will be renamed to `metric` in v1.4. :pr:`23470` by :user:`Meekail Zain `. :mod:`sklearn.datasets` ....................... - \|Enhancement\| Introduce the new parameter `parser` in :func:`datasets.fetch\_openml`. `parser="pandas"` allows to use the very CPU and memory efficient `pandas.read\_csv` parser to load dense ARFF formatted dataset files. It is possible to pass `parser="liac-arff"` to use the old LIAC parser. When `parser="auto"`, dense datasets are loaded with "pandas" and sparse datasets are loaded with "liac-arff". Currently, `parser="liac-arff"` by default and will change to `parser="auto"` in version 1.4 :pr:`21938` by :user:`Guillaume Lemaitre `. - \|Enhancement\| :func:`datasets.dump\_svmlight\_file` is now accelerated with a Cython implementation, providing 2-4x speedups. :pr:`23127` by :user:`Meekail Zain ` - \|Enhancement\| Path-like objects, such as those created with pathlib are now allowed as paths in :func:`datasets.load\_svmlight\_file` and :func:`datasets.load\_svmlight\_files`. :pr:`19075` by :user:`Carlos Ramos Carreño `. - \|Fix\| Make sure that :func:`datasets.fetch\_lfw\_people` and :func:`datasets.fetch\_lfw\_pairs` internally crop images based on the `slice\_` parameter. :pr:`24951` by :user:`Guillaume Lemaitre `. :mod:`sklearn.decomposition` ............................ - \|Efficiency\| :func:`decomposition.FastICA.fit` has been optimised w.r.t its memory footprint and runtime. :pr:`22268` by :user:`MohamedBsh `. - \|Enhancement\| :class:`decomposition.SparsePCA` and :class:`decomposition.MiniBatchSparsePCA` now implement an `inverse\_transform` function. :pr:`23905` by :user:`Guillaume Lemaitre `. - \|Enhancement\| :class:`decomposition.FastICA` now allows the user to select how whitening is performed through the new `whiten\_solver` parameter, which supports `svd` and `eigh`. `whiten\_solver` defaults to `svd` although `eigh` may be faster and more memory efficient in cases where `num\_features > num\_samples`. :pr:`11860` by :user:`Pierre Ablin `, :pr:`22527` by :user:`Meekail Zain ` and `Thomas Fan`\_. - \|Enhancement\| :class:`decomposition.LatentDirichletAllocation`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.0118599534034729, 0.08435384929180145, 0.008037400431931019, -0.03658007085323334, 0.06162506714463234, 0.016226867213845253, 0.0421917550265789, 0.030485305935144424, -0.03815045207738876, 0.043406348675489426, 0.04012972116470337, -0.014959071762859821, 0.05946580693125725, -0.0542312...	0.042666
select how whitening is performed through the new `whiten\_solver` parameter, which supports `svd` and `eigh`. `whiten\_solver` defaults to `svd` although `eigh` may be faster and more memory efficient in cases where `num\_features > num\_samples`. :pr:`11860` by :user:`Pierre Ablin `, :pr:`22527` by :user:`Meekail Zain ` and `Thomas Fan`\_. - \|Enhancement\| :class:`decomposition.LatentDirichletAllocation` now preserves dtype for `numpy.float32` input. :pr:`24528` by :user:`Takeshi Oura ` and :user:`Jérémie du Boisberranger `. - \|Fix\| Make sign of `components\_` deterministic in :class:`decomposition.SparsePCA`. :pr:`23935` by :user:`Guillaume Lemaitre `. - \|API\| The `n\_iter` parameter of :class:`decomposition.MiniBatchSparsePCA` is deprecated and replaced by the parameters `max\_iter`, `tol`, and `max\_no\_improvement` to be consistent with :class:`decomposition.MiniBatchDictionaryLearning`. `n\_iter` will be removed in version 1.3. :pr:`23726` by :user:`Guillaume Lemaitre `. - \|API\| The `n\_features\_` attribute of :class:`decomposition.PCA` is deprecated in favor of `n\_features\_in\_` and will be removed in 1.4. :pr:`24421` by :user:`Kshitij Mathur `. :mod:`sklearn.discriminant\_analysis` .................................... - \|MajorFeature\| :class:`discriminant\_analysis.LinearDiscriminantAnalysis` now supports the `Array API `\_ for `solver="svd"`. Array API support is considered experimental and might evolve without being subjected to our usual rolling deprecation cycle policy. See :ref:`array\_api` for more details. :pr:`22554` by `Thomas Fan`\_. - \|Fix\| Validate parameters only in `fit` and not in `\_\_init\_\_` for :class:`discriminant\_analysis.QuadraticDiscriminantAnalysis`. :pr:`24218` by :user:`Stefanie Molin `. :mod:`sklearn.ensemble` ....................... - \|MajorFeature\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` now support interaction constraints via the argument `interaction\_cst` of their constructors. :pr:`21020` by :user:`Christian Lorentzen `. Using interaction constraints also makes fitting faster. :pr:`24856` by :user:`Christian Lorentzen `. - \|Feature\| Adds `class\_weight` to :class:`ensemble.HistGradientBoostingClassifier`. :pr:`22014` by `Thomas Fan`\_. - \|Efficiency\| Improve runtime performance of :class:`ensemble.IsolationForest` by avoiding data copies. :pr:`23252` by :user:`Zhehao Liu `. - \|Enhancement\| :class:`ensemble.StackingClassifier` now accepts any kind of base estimator. :pr:`24538` by :user:`Guillem G Subies `. - \|Enhancement\| Make it possible to pass the `categorical\_features` parameter of :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` as feature names. :pr:`24889` by :user:`Olivier Grisel `. - \|Enhancement\| :class:`ensemble.StackingClassifier` now supports multilabel-indicator target :pr:`24146` by :user:`Nicolas Peretti `, :user:`Nestor Navarro `, :user:`Nati Tomattis `, and :user:`Vincent Maladiere `. - \|Enhancement\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` now accept their `monotonic\_cst` parameter to be passed as a dictionary in addition to the previously supported array-like format. Such dictionary have feature names as keys and one of `-1`, `0`, `1` as value to specify monotonicity constraints for each feature. :pr:`24855` by :user:`Olivier Grisel `. - \|Enhancement\| Interaction constraints for :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` can now be specified as strings for two common cases: "no\_interactions" and "pairwise" interactions. :pr:`24849` by :user:`Tim Head `. - \|Fix\| Fixed the issue where :class:`ensemble.AdaBoostClassifier` outputs NaN in feature importance when fitted with very small sample weight. :pr:`20415` by :user:`Zhehao Liu `. - \|Fix\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` no longer error when predicting on categories encoded as negative values and instead consider them a member of the "missing category". :pr:`24283` by `Thomas Fan`\_. - \|Fix\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`, with `verbose>=1`, print detailed timing information on computing histograms and finding best splits. The time spent in the root node was previously missing and is now included in the printed information. :pr:`24894` by :user:`Christian Lorentzen `. - \|API\| Rename the constructor parameter `base\_estimator` to `estimator` in the following classes: :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor`, :class:`ensemble.AdaBoostClassifier`, :class:`ensemble.AdaBoostRegressor`. `base\_estimator` is deprecated in 1.2 and will be removed in 1.4. :pr:`23819` by :user:`Adrian Trujillo ` and :user:`Edoardo Abati `. - \|API\| Rename the fitted attribute `base\_estimator\_` to `estimator\_` in the following classes: :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor`, :class:`ensemble.AdaBoostClassifier`, :class:`ensemble.AdaBoostRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`, :class:`ensemble.RandomTreesEmbedding`, :class:`ensemble.IsolationForest`. `base\_estimator\_` is deprecated in 1.2 and will be removed in 1.4. :pr:`23819` by :user:`Adrian Trujillo ` and :user:`Edoardo Abati `. :mod:`sklearn.feature\_selection` ................................ - \|Fix\| Fix a bug in :func:`feature\_selection.mutual\_info\_regression` and :func:`feature\_selection.mutual\_info\_classif`, where the continuous features in `X` should be scaled to a unit variance independently if the target	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.08270770311355591, 0.013611852191388607, -0.06389503926038742, 0.01446407102048397, 0.04026574268937111, -0.09221310913562775, -0.007288189604878426, 0.058706607669591904, -0.05110786110162735, 0.009229014627635479, 0.011967071332037449, -0.035899266600608826, -0.02390037663280964, -0.0...	0.120608
:class:`ensemble.RandomTreesEmbedding`, :class:`ensemble.IsolationForest`. `base\_estimator\_` is deprecated in 1.2 and will be removed in 1.4. :pr:`23819` by :user:`Adrian Trujillo ` and :user:`Edoardo Abati `. :mod:`sklearn.feature\_selection` ................................ - \|Fix\| Fix a bug in :func:`feature\_selection.mutual\_info\_regression` and :func:`feature\_selection.mutual\_info\_classif`, where the continuous features in `X` should be scaled to a unit variance independently if the target `y` is continuous or discrete. :pr:`24747` by :user:`Guillaume Lemaitre ` :mod:`sklearn.gaussian\_process` ............................... - \|Fix\| Fix :class:`gaussian\_process.kernels.Matern` gradient computation with `nu=0.5` for PyPy (and possibly other non CPython interpreters). :pr:`24245` by :user:`Loïc Estève `. - \|Fix\| The `fit` method of :class:`gaussian\_process.GaussianProcessRegressor` will not modify the input X in case a custom kernel is used, with a `diag` method that returns part of the input X. :pr:`24405` by :user:`Omar Salman `. :mod:`sklearn.impute` ..................... - \|Enhancement\| Added `keep\_empty\_features` parameter to :class:`impute.SimpleImputer`, :class:`impute.KNNImputer` and :class:`impute.IterativeImputer`, preventing removal of features containing only missing values when transforming. :pr:`16695` by :user:`Vitor Santa Rosa `. :mod:`sklearn.inspection` ......................... - \|MajorFeature\| Extended :func:`inspection.partial\_dependence` and :class:`inspection.PartialDependenceDisplay` to handle categorical features. :pr:`18298` by :user:`Madhura Jayaratne ` and :user:`Guillaume Lemaitre `. - \|Fix\| :class:`inspection.DecisionBoundaryDisplay` now raises error if input data is not 2-dimensional. :pr:`25077` by :user:`Arturo Amor `. :mod:`sklearn.kernel\_approximation` ................................... - \|Enhancement\| :class:`kernel\_approximation.RBFSampler` now preserves dtype for `numpy.float32` inputs. :pr:`24317` by `Tim Head `. - \|Enhancement\| :class:`kernel\_approximation.SkewedChi2Sampler` now preserves dtype for `numpy.float32` inputs. :pr:`24350` by :user:`Rahil Parikh `. - \|Enhancement\| :class:`kernel\_approximation.RBFSampler` now accepts `'scale'` option for parameter `gamma`. :pr:`24755` by :user:`Hleb Levitski `. :mod:`sklearn.linear\_model` ........................... - \|Enhancement\| :class:`linear\_model.LogisticRegression`, :class:`linear\_model.LogisticRegressionCV`, :class:`linear\_model.GammaRegressor`, :class:`linear\_model.PoissonRegressor` and :class:`linear\_model.TweedieRegressor` got a new solver `solver="newton-cholesky"`. This is a 2nd order (Newton) optimisation routine that uses a Cholesky decomposition of the hessian matrix. When `n\_samples >> n\_features`, the `"newton-cholesky"` solver has been observed to converge both faster and to a higher precision solution than the `"lbfgs"` solver on problems with one-hot encoded categorical variables with some rare categorical levels. :pr:`24637` and :pr:`24767` by :user:`Christian Lorentzen `. - \|Enhancement\| :class:`linear\_model.GammaRegressor`, :class:`linear\_model.PoissonRegressor` and :class:`linear\_model.TweedieRegressor` can reach higher precision with the lbfgs solver, in particular when `tol` is set to a tiny value. Moreover, `verbose` is now properly propagated to L-BFGS-B. :pr:`23619` by :user:`Christian Lorentzen `. - \|Fix\| :class:`linear\_model.SGDClassifier` and :class:`linear\_model.SGDRegressor` will raise an error when all the validation samples have zero sample weight. :pr:`23275` by `Zhehao Liu `. - \|Fix\| :class:`linear\_model.SGDOneClassSVM` no longer performs parameter validation in the constructor. All validation is now handled in `fit()` and `partial\_fit()`. :pr:`24433` by :user:`Yogendrasingh `, :user:`Arisa Y. ` and :user:`Tim Head `. - \|Fix\| Fix average loss calculation when early stopping is enabled in :class:`linear\_model.SGDRegressor` and :class:`linear\_model.SGDClassifier`. Also updated the condition for early stopping accordingly. :pr:`23798` by :user:`Harsh Agrawal `. - \|API\| The default value for the `solver` parameter in :class:`linear\_model.QuantileRegressor` will change from `"interior-point"` to `"highs"` in version 1.4. :pr:`23637` by :user:`Guillaume Lemaitre `. - \|API\| String option `"none"` is deprecated for `penalty` argument in :class:`linear\_model.LogisticRegression`, and will be removed in version 1.4. Use `None` instead. :pr:`23877` by :user:`Zhehao Liu `. - \|API\| The default value of `tol` was changed from `1e-3` to `1e-4` for :func:`linear\_model.ridge\_regression`, :class:`linear\_model.Ridge` and :class:`linear\_model.RidgeClassifier`. :pr:`24465` by :user:`Christian Lorentzen `. :mod:`sklearn.manifold` ....................... - \|Feature\| Adds option to use the normalized stress in :class:`manifold.MDS`. This is enabled by setting the new `normalize` parameter to `True`. :pr:`10168` by :user:`Łukasz Borchmann `, :pr:`12285` by :user:`Matthias Miltenberger `, :pr:`13042` by :user:`Matthieu Parizy `, :pr:`18094` by :user:`Roth E Conrad ` and :pr:`22562` by :user:`Meekail Zain `. - \|Enhancement\| Adds `eigen\_tol` parameter to :class:`manifold.SpectralEmbedding`. Both :func:`manifold.spectral\_embedding` and :class:`manifold.SpectralEmbedding` now propagate `eigen\_tol` to all choices of `eigen\_solver`. Includes a new option `eigen\_tol="auto"` and begins deprecation to change the default from `eigen\_tol=0` to `eigen\_tol="auto"` in version 1.3. :pr:`23210` by :user:`Meekail Zain `. - \|Enhancement\| :class:`manifold.Isomap` now preserves dtype for	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.05648139491677284, -0.058777227997779846, 0.041234299540519714, 0.04642738774418831, 0.1441417634487152, -0.04859716445207596, 0.04607135429978371, -0.007397168781608343, -0.05966115742921829, 0.015561241656541824, 0.07452622801065445, -0.02700076811015606, -0.0036025913432240486, -0.06...	0.03941
- \|Enhancement\| Adds `eigen\_tol` parameter to :class:`manifold.SpectralEmbedding`. Both :func:`manifold.spectral\_embedding` and :class:`manifold.SpectralEmbedding` now propagate `eigen\_tol` to all choices of `eigen\_solver`. Includes a new option `eigen\_tol="auto"` and begins deprecation to change the default from `eigen\_tol=0` to `eigen\_tol="auto"` in version 1.3. :pr:`23210` by :user:`Meekail Zain `. - \|Enhancement\| :class:`manifold.Isomap` now preserves dtype for `np.float32` inputs. :pr:`24714` by :user:`Rahil Parikh `. - \|API\| Added an `"auto"` option to the `normalized\_stress` argument in :class:`manifold.MDS` and :func:`manifold.smacof`. Note that `normalized\_stress` is only valid for non-metric MDS, therefore the `"auto"` option enables `normalized\_stress` when `metric=False` and disables it when `metric=True`. `"auto"` will become the default value for `normalized\_stress` in version 1.4. :pr:`23834` by :user:`Meekail Zain ` :mod:`sklearn.metrics` ...................... - \|Feature\| :func:`metrics.ConfusionMatrixDisplay.from\_estimator`, :func:`metrics.ConfusionMatrixDisplay.from\_predictions`, and :meth:`metrics.ConfusionMatrixDisplay.plot` accepts a `text\_kw` parameter which is passed to matplotlib's `text` function. :pr:`24051` by `Thomas Fan`\_. - \|Feature\| :func:`metrics.class\_likelihood\_ratios` is added to compute the positive and negative likelihood ratios derived from the confusion matrix of a binary classification problem. :pr:`22518` by :user:`Arturo Amor `. - \|Feature\| Add :class:`metrics.PredictionErrorDisplay` to plot residuals vs predicted and actual vs predicted to qualitatively assess the behavior of a regressor. The display can be created with the class methods :func:`metrics.PredictionErrorDisplay.from\_estimator` and :func:`metrics.PredictionErrorDisplay.from\_predictions`. :pr:`18020` by :user:`Guillaume Lemaitre `. - \|Feature\| :func:`metrics.roc\_auc\_score` now supports micro-averaging (`average="micro"`) for the One-vs-Rest multiclass case (`multi\_class="ovr"`). :pr:`24338` by :user:`Arturo Amor `. - \|Enhancement\| Adds an `"auto"` option to `eps` in :func:`metrics.log\_loss`. This option will automatically set the `eps` value depending on the data type of `y\_pred`. In addition, the default value of `eps` is changed from `1e-15` to the new `"auto"` option. :pr:`24354` by :user:`Safiuddin Khaja ` and :user:`gsiisg `. - \|Fix\| Allows `csr\_matrix` as input for parameter: `y\_true` of the :func:`metrics.label\_ranking\_average\_precision\_score` metric. :pr:`23442` by :user:`Sean Atukorala ` - \|Fix\| :func:`metrics.ndcg\_score` will now trigger a warning when the `y\_true` value contains a negative value. Users may still use negative values, but the result may not be between 0 and 1. Starting in v1.4, passing in negative values for `y\_true` will raise an error. :pr:`22710` by :user:`Conroy Trinh ` and :pr:`23461` by :user:`Meekail Zain `. - \|Fix\| :func:`metrics.log\_loss` with `eps=0` now returns a correct value of 0 or `np.inf` instead of `nan` for predictions at the boundaries (0 or 1). It also accepts integer input. :pr:`24365` by :user:`Christian Lorentzen `. - \|API\| The parameter `sum\_over\_features` of :func:`metrics.pairwise.manhattan\_distances` is deprecated and will be removed in 1.4. :pr:`24630` by :user:`Rushil Desai `. :mod:`sklearn.model\_selection` .............................. - \|Feature\| Added the class :class:`model\_selection.LearningCurveDisplay` that allows to make easy plotting of learning curves obtained by the function :func:`model\_selection.learning\_curve`. :pr:`24084` by :user:`Guillaume Lemaitre `. - \|Fix\| For all `SearchCV` classes and scipy >= 1.10, rank corresponding to a nan score is correctly set to the maximum possible rank, rather than `np.iinfo(np.int32).min`. :pr:`24141` by :user:`Loïc Estève `. - \|Fix\| In both :class:`model\_selection.HalvingGridSearchCV` and :class:`model\_selection.HalvingRandomSearchCV` parameter combinations with a NaN score now share the lowest rank. :pr:`24539` by :user:`Tim Head `. - \|Fix\| For :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` ranks corresponding to nan scores will all be set to the maximum possible rank. :pr:`24543` by :user:`Guillaume Lemaitre `. :mod:`sklearn.multioutput` .......................... - \|Feature\| Added boolean `verbose` flag to classes: :class:`multioutput.ClassifierChain` and :class:`multioutput.RegressorChain`. :pr:`23977` by :user:`Eric Fiegel `, :user:`Chiara Marmo `, :user:`Lucy Liu `, and :user:`Guillaume Lemaitre `. :mod:`sklearn.naive\_bayes` .......................... - \|Feature\| Add methods `predict\_joint\_log\_proba` to all naive Bayes classifiers. :pr:`23683` by :user:`Andrey Melnik `. - \|Enhancement\| A new parameter `force\_alpha` was added to :class:`naive\_bayes.BernoulliNB`, :class:`naive\_bayes.ComplementNB`, :class:`naive\_bayes.CategoricalNB`, and :class:`naive\_bayes.MultinomialNB`, allowing user to set parameter alpha to a very small number, greater or equal 0, which was earlier automatically changed to `1e-10` instead. :pr:`16747` by :user:`arka204`, :pr:`18805` by :user:`hongshaoyang`, :pr:`22269` by :user:`Meekail Zain `. :mod:`sklearn.neighbors` ........................ - \|Feature\| Adds new	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.09810170531272888, -0.027057820931077003, -0.0527554489672184, 0.021762937307357788, -0.024617839604616165, -0.02845202013850212, -0.01120568998157978, -0.029862556606531143, -0.09244837611913681, 0.016060255467891693, 0.04104549065232277, -0.07029499113559723, -0.03605234622955322, 0.0...	0.072023
parameter `force\_alpha` was added to :class:`naive\_bayes.BernoulliNB`, :class:`naive\_bayes.ComplementNB`, :class:`naive\_bayes.CategoricalNB`, and :class:`naive\_bayes.MultinomialNB`, allowing user to set parameter alpha to a very small number, greater or equal 0, which was earlier automatically changed to `1e-10` instead. :pr:`16747` by :user:`arka204`, :pr:`18805` by :user:`hongshaoyang`, :pr:`22269` by :user:`Meekail Zain `. :mod:`sklearn.neighbors` ........................ - \|Feature\| Adds new function :func:`neighbors.sort\_graph\_by\_row\_values` to sort a CSR sparse graph such that each row is stored with increasing values. This is useful to improve efficiency when using precomputed sparse distance matrices in a variety of estimators and avoid an `EfficiencyWarning`. :pr:`23139` by `Tom Dupre la Tour`\_. - \|Efficiency\| :class:`neighbors.NearestCentroid` is faster and requires less memory as it better leverages CPUs' caches to compute predictions. :pr:`24645` by :user:`Olivier Grisel `. - \|Enhancement\| :class:`neighbors.KernelDensity` bandwidth parameter now accepts definition using Scott's and Silverman's estimation methods. :pr:`10468` by :user:`Ruben ` and :pr:`22993` by :user:`Jovan Stojanovic `. - \|Enhancement\| `neighbors.NeighborsBase` now accepts Minkowski semi-metric (i.e. when :math:`0 < p < 1` for `metric="minkowski"`) for `algorithm="auto"` or `algorithm="brute"`. :pr:`24750` by :user:`Rudresh Veerkhare ` - \|Fix\| :class:`neighbors.NearestCentroid` now raises an informative error message at fit-time instead of failing with a low-level error message at predict-time. :pr:`23874` by :user:`Juan Gomez <2357juan>`. - \|Fix\| Set `n\_jobs=None` by default (instead of `1`) for :class:`neighbors.KNeighborsTransformer` and :class:`neighbors.RadiusNeighborsTransformer`. :pr:`24075` by :user:`Valentin Laurent `. - \|Enhancement\| :class:`neighbors.LocalOutlierFactor` now preserves dtype for `numpy.float32` inputs. :pr:`22665` by :user:`Julien Jerphanion `. :mod:`sklearn.neural\_network` ............................. - \|Fix\| :class:`neural\_network.MLPClassifier` and :class:`neural\_network.MLPRegressor` always expose the parameters `best\_loss\_`, `validation\_scores\_`, and `best\_validation\_score\_`. `best\_loss\_` is set to `None` when `early\_stopping=True`, while `validation\_scores\_` and `best\_validation\_score\_` are set to `None` when `early\_stopping=False`. :pr:`24683` by :user:`Guillaume Lemaitre `. :mod:`sklearn.pipeline` ....................... - \|Enhancement\| :meth:`pipeline.FeatureUnion.get\_feature\_names\_out` can now be used when one of the transformers in the :class:`pipeline.FeatureUnion` is `"passthrough"`. :pr:`24058` by :user:`Diederik Perdok ` - \|Enhancement\| The :class:`pipeline.FeatureUnion` class now has a `named\_transformers` attribute for accessing transformers by name. :pr:`20331` by :user:`Christopher Flynn `. :mod:`sklearn.preprocessing` ............................ - \|Enhancement\| :class:`preprocessing.FunctionTransformer` will always try to set `n\_features\_in\_` and `feature\_names\_in\_` regardless of the `validate` parameter. :pr:`23993` by `Thomas Fan`\_. - \|Fix\| :class:`preprocessing.LabelEncoder` correctly encodes NaNs in `transform`. :pr:`22629` by `Thomas Fan`\_. - \|API\| The `sparse` parameter of :class:`preprocessing.OneHotEncoder` is now deprecated and will be removed in version 1.4. Use `sparse\_output` instead. :pr:`24412` by :user:`Rushil Desai `. :mod:`sklearn.svm` .................. - \|API\| The `class\_weight\_` attribute is now deprecated for :class:`svm.NuSVR`, :class:`svm.SVR`, :class:`svm.OneClassSVM`. :pr:`22898` by :user:`Meekail Zain `. :mod:`sklearn.tree` ................... - \|Enhancement\| :func:`tree.plot\_tree`, :func:`tree.export\_graphviz` now uses a lower case `x[i]` to represent feature `i`. :pr:`23480` by `Thomas Fan`\_. :mod:`sklearn.utils` .................... - \|Feature\| A new module exposes development tools to discover estimators (i.e. :func:`utils.discovery.all\_estimators`), displays (i.e. :func:`utils.discovery.all\_displays`) and functions (i.e. :func:`utils.discovery.all\_functions`) in scikit-learn. :pr:`21469` by :user:`Guillaume Lemaitre `. - \|Enhancement\| :func:`utils.extmath.randomized\_svd` now accepts an argument, `lapack\_svd\_driver`, to specify the lapack driver used in the internal deterministic SVD used by the randomized SVD algorithm. :pr:`20617` by :user:`Srinath Kailasa ` - \|Enhancement\| :func:`utils.validation.column\_or\_1d` now accepts a `dtype` parameter to specific `y`'s dtype. :pr:`22629` by `Thomas Fan`\_. - \|Enhancement\| `utils.extmath.cartesian` now accepts arrays with different `dtype` and will cast the output to the most permissive `dtype`. :pr:`25067` by :user:`Guillaume Lemaitre `. - \|Fix\| :func:`utils.multiclass.type\_of\_target` now properly handles sparse matrices. :pr:`14862` by :user:`Léonard Binet `. - \|Fix\| HTML representation no longer errors when an estimator class is a value in `get\_params`. :pr:`24512` by `Thomas Fan`\_. - \|Fix\| :func:`utils.estimator\_checks.check\_estimator` now takes into account the `requires\_positive\_X` tag correctly. :pr:`24667` by `Thomas Fan`\_. - \|Fix\| :func:`utils.check\_array` now supports Pandas Series with `pd.NA` by raising a better error message or returning a compatible `ndarray`. :pr:`25080` by `Thomas Fan`\_. - \|API\| The extra keyword parameters of :func:`utils.extmath.density` are deprecated and will be removed in 1.4. :pr:`24523` by :user:`Mia Bajic `. .. rubric:: Code and documentation	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.009422759525477886, -0.00961657427251339, -0.11068572849035263, -0.032438747584819794, -0.009936498478055, 0.011378983967006207, 0.0005615090485662222, -0.0019520592177286744, -0.10372253507375717, 0.03266464173793793, 0.07335034757852554, -0.016139160841703415, 0.021932577714323997, -0...	0.003973
- \|Fix\| :func:`utils.check\_array` now supports Pandas Series with `pd.NA` by raising a better error message or returning a compatible `ndarray`. :pr:`25080` by `Thomas Fan`\_. - \|API\| The extra keyword parameters of :func:`utils.extmath.density` are deprecated and will be removed in 1.4. :pr:`24523` by :user:`Mia Bajic `. .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.1, including: 2357juan, 3lLobo, Adam J. Stewart, Adam Kania, Adam Li, Aditya Anulekh, Admir Demiraj, adoublet, Adrin Jalali, Ahmedbgh, Aiko, Akshita Prasanth, Ala-Na, Alessandro Miola, Alex, Alexandr, Alexandre Perez-Lebel, Alex Buzenet, Ali H. El-Kassas, aman kumar, Amit Bera, András Simon, Andreas Grivas, Andreas Mueller, Andrew Wang, angela-maennel, Aniket Shirsat, Anthony22-dev, Antony Lee, anupam, Apostolos Tsetoglou, Aravindh R, Artur Hermano, Arturo Amor, as-90, ashah002, Ashwin Mathur, avm19, Azaria Gebremichael, b0rxington, Badr MOUFAD, Bardiya Ak, Bartłomiej Gońda, BdeGraaff, Benjamin Bossan, Benjamin Carter, berkecanrizai, Bernd Fritzke, Bhoomika, Biswaroop Mitra, Brandon TH Chen, Brett Cannon, Bsh, cache-missing, carlo, Carlos Ramos Carreño, ceh, chalulu, Changyao Chen, Charles Zablit, Chiara Marmo, Christian Lorentzen, Christian Ritter, Christian Veenhuis, christianwaldmann, Christine P. Chai, Claudio Salvatore Arcidiacono, Clément Verrier, crispinlogan, Da-Lan, DanGonite57, Daniela Fernandes, DanielGaerber, darioka, Darren Nguyen, davidblnc, david-cortes, David Gilbertson, David Poznik, Dayne, Dea María Léon, Denis, Dev Khant, Dhanshree Arora, Diadochokinetic, diederikwp, Dimitri Papadopoulos Orfanos, Dimitris Litsidis, drewhogg, Duarte OC, Dwight Lindquist, Eden Brekke, Edern, Edoardo Abati, Eleanore Denies, EliaSchiavon, Emir, ErmolaevPA, Fabrizio Damicelli, fcharras, Felipe Siola, Flynn, francesco-tuveri, Franck Charras, ftorres16, Gael Varoquaux, Geevarghese George, genvalen, GeorgiaMayDay, Gianr Lazz, Hleb Levitski, Glòria Macià Muñoz, Guillaume Lemaitre, Guillem García Subies, Guitared, gunesbayir, Haesun Park, Hansin Ahuja, Hao Chun Chang, Harsh Agrawal, harshit5674, hasan-yaman, henrymooresc, Henry Sorsky, Hristo Vrigazov, htsedebenham, humahn, i-aki-y, Ian Thompson, Ido M, Iglesys, Iliya Zhechev, Irene, ivanllt, Ivan Sedykh, Jack McIvor, jakirkham, JanFidor, Jason G, Jérémie du Boisberranger, Jiten Sidhpura, jkarolczak, João David, JohnathanPi, John Koumentis, John P, John Pangas, johnthagen, Jordan Fleming, Joshua Choo Yun Keat, Jovan Stojanovic, Juan Carlos Alfaro Jiménez, juanfe88, Juan Felipe Arias, JuliaSchoepp, Julien Jerphanion, jygerardy, ka00ri, Kanishk Sachdev, Kanissh, Kaushik Amar Das, Kendall, Kenneth Prabakaran, Kento Nozawa, kernc, Kevin Roice, Kian Eliasi, Kilian Kluge, Kilian Lieret, Kirandevraj, Kraig, krishna kumar, krishna vamsi, Kshitij Kapadni, Kshitij Mathur, Lauren Burke, Léonard Binet, lingyi1110, Lisa Casino, Logan Thomas, Loic Esteve, Luciano Mantovani, Lucy Liu, Maascha, Madhura Jayaratne, madinak, Maksym, Malte S. Kurz, Mansi Agrawal, Marco Edward Gorelli, Marco Wurps, Maren Westermann, Maria Telenczuk, Mario Kostelac, martin-kokos, Marvin Krawutschke, Masanori Kanazu, mathurinm, Matt Haberland, mauroantonioserrano, Max Halford, Maxi Marufo, maximeSaur, Maxim Smolskiy, Maxwell, m. bou, Meekail Zain, Mehgarg, mehmetcanakbay, Mia Bajić, Michael Flaks, Michael Hornstein, Michel de Ruiter, Michelle Paradis, Mikhail Iljin, Misa Ogura, Moritz Wilksch, mrastgoo, Naipawat Poolsawat, Naoise Holohan, Nass, Nathan Jacobi, Nawazish Alam, Nguyễn Văn Diễn, Nicola Fanelli, Nihal Thukarama Rao, Nikita Jare, nima10khodaveisi, Nima Sarajpoor, nitinramvelraj, NNLNR, npache, Nwanna-Joseph, Nymark Kho, o-holman, Olivier Grisel, Olle Lukowski, Omar Hassoun, Omar Salman, osman tamer, ouss1508, Oyindamola Olatunji, PAB, Pandata, partev, Paulo Sergio Soares, Petar Mlinarić, Peter Jansson, Peter Steinbach, Philipp Jung, Piet Brömmel, Pooja M, Pooja Subramaniam, priyam kakati, puhuk, Rachel Freeland, Rachit Keerti Das, Rafal Wojdyla, Raghuveer Bhat, Rahil Parikh, Ralf Gommers, ram vikram singh, Ravi Makhija, Rehan Guha, Reshama Shaikh, Richard Klima, Rob Crockett, Robert Hommes, Robert Juergens, Robin Lenz, Rocco Meli, Roman4oo, Ross Barnowski, Rowan Mankoo, Rudresh Veerkhare, Rushil Desai, Sabri Monaf Sabri, Safikh, Safiuddin Khaja, Salahuddin, Sam Adam Day, Sandra Yojana Meneses, Sandro Ephrem, Sangam, SangamSwadik, SANJAI\_3, SarahRemus, Sashka Warner, SavkoMax, Scott Gigante, Scott Gustafson, Sean Atukorala, sec65, SELEE, seljaks, Shady el Gewily, Shane, shellyfung, Shinsuke Mori, Shiva chauhan, Shoaib Khan, Shogo Hida, Shrankhla Srivastava, Shuangchi He, Simon,	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.0808853805065155, -0.0852210745215416, -0.09896370023488998, -0.030671769753098488, 0.005066267214715481, -0.08114718645811081, 0.005762335378676653, -0.05119580030441284, -0.06243445724248886, 0.033583201467990875, 0.047177016735076904, -0.0062670717015862465, -0.03810077905654907, 0.0...	0.136068
Desai, Sabri Monaf Sabri, Safikh, Safiuddin Khaja, Salahuddin, Sam Adam Day, Sandra Yojana Meneses, Sandro Ephrem, Sangam, SangamSwadik, SANJAI\_3, SarahRemus, Sashka Warner, SavkoMax, Scott Gigante, Scott Gustafson, Sean Atukorala, sec65, SELEE, seljaks, Shady el Gewily, Shane, shellyfung, Shinsuke Mori, Shiva chauhan, Shoaib Khan, Shogo Hida, Shrankhla Srivastava, Shuangchi He, Simon, sonnivs, Sortofamudkip, Srinath Kailasa, Stanislav (Stanley) Modrak, Stefanie Molin, stellalin7, Stéphane Collot, Steven Van Vaerenbergh, Steve Schmerler, Sven Stehle, Tabea Kossen, TheDevPanda, the-syd-sre, Thijs van Weezel, Thomas Bonald, Thomas Germer, Thomas J. Fan, Ti-Ion, Tim Head, Timofei Kornev, toastedyeast, Tobias Pitters, Tom Dupré la Tour, tomiock, Tom Mathews, Tom McTiernan, tspeng, Tyler Egashira, Valentin Laurent, Varun Jain, Vera Komeyer, Vicente Reyes-Puerta, Vinayak Mehta, Vincent M, Vishal, Vyom Pathak, wattai, wchathura, WEN Hao, William M, x110, Xiao Yuan, Xunius, yanhong-zhao-ef, Yusuf Raji, Z Adil Khwaja, zeeshan lone	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.2.rst	main	scikit-learn	[ -0.03446034714579582, -0.06566594541072845, -0.018332690000534058, -0.004466201178729534, -0.026325516402721405, 0.10881499946117401, -0.008198502473533154, 0.05490340292453766, -0.017191842198371887, 0.0005108923651278019, -0.006042927969247103, -0.022071408107876778, -0.025496531277894974,...	0.123557
.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_0: =========== Version 1.0 =========== For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_1\_0\_0.py`. .. include:: changelog\_legend.inc .. \_changes\_1\_0\_2: Version 1.0.2 ============= \\December 2021\\ - \|Fix\| :class:`cluster.Birch`, :class:`feature\_selection.RFECV`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.GradientBoostingRegressor`, and :class:`ensemble.GradientBoostingClassifier` do not raise warning when fitted on a pandas DataFrame anymore. :pr:`21578` by `Thomas Fan`\_. Changelog --------- :mod:`sklearn.cluster` ...................... - \|Fix\| Fixed an infinite loop in :func:`cluster.SpectralClustering` by moving an iteration counter from try to except. :pr:`21271` by :user:`Tyler Martin `. :mod:`sklearn.datasets` ....................... - \|Fix\| :func:`datasets.fetch\_openml` is now thread safe. Data is first downloaded to a temporary subfolder and then renamed. :pr:`21833` by :user:`Siavash Rezazadeh `. :mod:`sklearn.decomposition` ............................ - \|Fix\| Fixed the constraint on the objective function of :class:`decomposition.DictionaryLearning`, :class:`decomposition.MiniBatchDictionaryLearning`, :class:`decomposition.SparsePCA` and :class:`decomposition.MiniBatchSparsePCA` to be convex and match the referenced article. :pr:`19210` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.ensemble` ....................... - \|Fix\| :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`, and :class:`ensemble.RandomTreesEmbedding` now raise a ``ValueError`` when ``bootstrap=False`` and ``max\_samples`` is not ``None``. :pr:`21295` :user:`Haoyin Xu `. - \|Fix\| Solve a bug in :class:`ensemble.GradientBoostingClassifier` where the exponential loss was computing the positive gradient instead of the negative one. :pr:`22050` by :user:`Guillaume Lemaitre `. :mod:`sklearn.feature\_selection` ................................ - \|Fix\| Fixed :class:`feature\_selection.SelectFromModel` by improving support for base estimators that do not set `feature\_names\_in\_`. :pr:`21991` by `Thomas Fan`\_. :mod:`sklearn.impute` ..................... - \|Fix\| Fix a bug in :class:`linear\_model.RidgeClassifierCV` where the method `predict` was performing an `argmax` on the scores obtained from `decision\_function` instead of returning the multilabel indicator matrix. :pr:`19869` by :user:`Guillaume Lemaitre `. :mod:`sklearn.linear\_model` ........................... - \|Fix\| :class:`linear\_model.LassoLarsIC` now correctly computes AIC and BIC. An error is now raised when `n\_features > n\_samples` and when the noise variance is not provided. :pr:`21481` by :user:`Guillaume Lemaitre ` and :user:`Andrés Babino `. :mod:`sklearn.manifold` ....................... - \|Fix\| Fixed an unnecessary error when fitting :class:`manifold.Isomap` with a precomputed dense distance matrix where the neighbors graph has multiple disconnected components. :pr:`21915` by `Tom Dupre la Tour`\_. :mod:`sklearn.metrics` ...................... - \|Fix\| All :class:`sklearn.metrics.DistanceMetric` subclasses now correctly support read-only buffer attributes. This fixes a regression introduced in 1.0.0 with respect to 0.24.2. :pr:`21694` by :user:`Julien Jerphanion `. - \|Fix\| All `sklearn.metrics.MinkowskiDistance` now accepts a weight parameter that makes it possible to write code that behaves consistently both with scipy 1.8 and earlier versions. In turn this means that all neighbors-based estimators (except those that use `algorithm="kd\_tree"`) now accept a weight parameter with `metric="minkowski"` to yield results that are always consistent with `scipy.spatial.distance.cdist`. :pr:`21741` by :user:`Olivier Grisel `. :mod:`sklearn.multiclass` ......................... - \|Fix\| :meth:`multiclass.OneVsRestClassifier.predict\_proba` does not error when fitted on constant integer targets. :pr:`21871` by `Thomas Fan`\_. :mod:`sklearn.neighbors` ........................ - \|Fix\| :class:`neighbors.KDTree` and :class:`neighbors.BallTree` correctly support read-only buffer attributes. :pr:`21845` by `Thomas Fan`\_. :mod:`sklearn.preprocessing` ............................ - \|Fix\| Fixes compatibility bug with NumPy 1.22 in :class:`preprocessing.OneHotEncoder`. :pr:`21517` by `Thomas Fan`\_. :mod:`sklearn.tree` ................... - \|Fix\| Prevents :func:`tree.plot\_tree` from drawing out of the boundary of the figure. :pr:`21917` by `Thomas Fan`\_. - \|Fix\| Support loading pickles of decision tree models when the pickle has been generated on a platform with a different bitness. A typical example is to train and pickle the model on 64 bit machine and load the model on a 32 bit machine for prediction. :pr:`21552` by :user:`Loïc Estève `. :mod:`sklearn.utils` .................... - \|Fix\| :func:`utils.estimator\_html\_repr` now escapes all the estimator descriptions in the generated HTML. :pr:`21493` by :user:`Aurélien Geron `. .. \_changes\_1\_0\_1: Version 1.0.1 ============= \\October 2021\\ Fixed models ------------ - \|Fix\| Non-fit methods in the following classes do not raise a UserWarning when fitted on DataFrames with valid feature names: :class:`covariance.EllipticEnvelope`, :class:`ensemble.IsolationForest`, :class:`ensemble.AdaBoostClassifier`, :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.RadiusNeighborsRegressor`. :pr:`21199` by `Thomas Fan`\_. :mod:`sklearn.calibration` .......................... - \|Fix\| Fixed :class:`calibration.CalibratedClassifierCV` to take into account	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.03550073131918907, -0.03642592206597328, 0.04598504677414894, -0.002055169316008687, 0.1519014984369278, 0.03712524473667145, -0.0051566241309046745, -0.011769738048315048, -0.02433818392455578, 0.007462291978299618, 0.06286057829856873, -0.043286338448524475, -0.057357363402843475, -0....	0.042002
Version 1.0.1 ============= \\October 2021\\ Fixed models ------------ - \|Fix\| Non-fit methods in the following classes do not raise a UserWarning when fitted on DataFrames with valid feature names: :class:`covariance.EllipticEnvelope`, :class:`ensemble.IsolationForest`, :class:`ensemble.AdaBoostClassifier`, :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.RadiusNeighborsRegressor`. :pr:`21199` by `Thomas Fan`\_. :mod:`sklearn.calibration` .......................... - \|Fix\| Fixed :class:`calibration.CalibratedClassifierCV` to take into account `sample\_weight` when computing the base estimator prediction when `ensemble=False`. :pr:`20638` by :user:`Julien Bohné `. - \|Fix\| Fixed a bug in :class:`calibration.CalibratedClassifierCV` with `method="sigmoid"` that was ignoring the `sample\_weight` when computing the Bayesian priors. :pr:`21179` by :user:`Guillaume Lemaitre `. :mod:`sklearn.cluster` ...................... - \|Fix\| Fixed a bug in :class:`cluster.KMeans`, ensuring reproducibility and equivalence between sparse and dense input. :pr:`21195` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.ensemble` ....................... - \|Fix\| Fixed a bug that could produce a segfault in rare cases for :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`. :pr:`21130` :user:`Christian Lorentzen `. :mod:`sklearn.gaussian\_process` ............................... - \|Fix\| Compute `y\_std` properly with multi-target in :class:`sklearn.gaussian\_process.GaussianProcessRegressor` allowing proper normalization in multi-target scene. :pr:`20761` by :user:`Patrick de C. T. R. Ferreira `. :mod:`sklearn.feature\_extraction` ................................. - \|Efficiency\| Fixed an efficiency regression introduced in version 1.0.0 in the `transform` method of :class:`feature\_extraction.text.CountVectorizer` which no longer checks for uppercase characters in the provided vocabulary. :pr:`21251` by :user:`Jérémie du Boisberranger `. - \|Fix\| Fixed a bug in :class:`feature\_extraction.text.CountVectorizer` and :class:`feature\_extraction.text.TfidfVectorizer` by raising an error when 'min\_idf' or 'max\_idf' are floating-point numbers greater than 1. :pr:`20752` by :user:`Alek Lefebvre `. :mod:`sklearn.linear\_model` ........................... - \|Fix\| Improves stability of :class:`linear\_model.LassoLars` for different versions of openblas. :pr:`21340` by `Thomas Fan`\_. - \|Fix\| :class:`linear\_model.LogisticRegression` now raises a better error message when the solver does not support sparse matrices with int64 indices. :pr:`21093` by `Tom Dupre la Tour`\_. :mod:`sklearn.neighbors` ........................ - \|Fix\| :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.RadiusNeighborsRegressor` with `metric="precomputed"` raises an error for `bsr` and `dok` sparse matrices in methods: `fit`, `kneighbors` and `radius\_neighbors`, due to handling of explicit zeros in `bsr` and `dok` :term:`sparse graph` formats. :pr:`21199` by `Thomas Fan`\_. :mod:`sklearn.pipeline` ....................... - \|Fix\| :meth:`pipeline.Pipeline.get\_feature\_names\_out` correctly passes feature names out from one step of a pipeline to the next. :pr:`21351` by `Thomas Fan`\_. :mod:`sklearn.svm` .................. - \|Fix\| :class:`svm.SVC` and :class:`svm.SVR` check for an inconsistency in its internal representation and raise an error instead of segfaulting. This fix also resolves `CVE-2020-28975 `\_\_. :pr:`21336` by `Thomas Fan`\_. :mod:`sklearn.utils` .................... - \|Enhancement\| `utils.validation.\_check\_sample\_weight` can perform a non-negativity check on the sample weights. It can be turned on using the only\_non\_negative bool parameter. Estimators that check for non-negative weights are updated: :func:`linear\_model.LinearRegression` (here the previous error message was misleading), :func:`ensemble.AdaBoostClassifier`, :func:`ensemble.AdaBoostRegressor`, :func:`neighbors.KernelDensity`. :pr:`20880` by :user:`Guillaume Lemaitre ` and :user:`András Simon `. - \|Fix\| Solve a bug in ``sklearn.utils.metaestimators.if\_delegate\_has\_method`` where the underlying check for an attribute did not work with NumPy arrays. :pr:`21145` by :user:`Zahlii `. Miscellaneous ............. - \|Fix\| Fitting an estimator on a dataset that has no feature names, that was previously fitted on a dataset with feature names no longer keeps the old feature names stored in the `feature\_names\_in\_` attribute. :pr:`21389` by :user:`Jérémie du Boisberranger `. .. \_changes\_1\_0: Version 1.0.0 ============= \\September 2021\\ Minimal dependencies -------------------- Version 1.0.0 of scikit-learn requires python 3.7+, numpy 1.14.6+ and scipy 1.1.0+. Optional minimal dependency is matplotlib 2.2.2+. Enforcing keyword-only arguments -------------------------------- In an effort to promote clear and non-ambiguous use of the library, most constructor and function parameters must now be passed as keyword arguments (i.e. using the `param=value` syntax) instead of positional. If a keyword-only parameter is used as positional, a `TypeError` is now raised. :issue:`15005` :pr:`20002` by `Joel Nothman`\_, `Adrin Jalali`\_, `Thomas Fan`\_, `Nicolas Hug`\_, and `Tom Dupre la Tour`\_. See `SLEP009 `\_ for more details. Changed models -------------- The following estimators and functions, when fit with the same data and parameters, may produce	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.010391377843916416, -0.06159269064664841, 0.0548490472137928, 0.019070196896791458, 0.1047106608748436, -0.05657472461462021, -0.0059511130675673485, -0.060541145503520966, -0.10259262472391129, -0.00004728223939309828, 0.007322175428271294, -0.0784401074051857, 0.02964891493320465, -0....	0.019685
parameter is used as positional, a `TypeError` is now raised. :issue:`15005` :pr:`20002` by `Joel Nothman`\_, `Adrin Jalali`\_, `Thomas Fan`\_, `Nicolas Hug`\_, and `Tom Dupre la Tour`\_. See `SLEP009 `\_ for more details. 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. - \|Fix\| :class:`manifold.TSNE` now avoids numerical underflow issues during affinity matrix computation. - \|Fix\| :class:`manifold.Isomap` now connects disconnected components of the neighbors graph along some minimum distance pairs, instead of changing every infinite distances to zero. - \|Fix\| The splitting criterion of :class:`tree.DecisionTreeClassifier` and :class:`tree.DecisionTreeRegressor` can be impacted by a fix in the handling of rounding errors. Previously some extra spurious splits could occur. - \|Fix\| :func:`model\_selection.train\_test\_split` with a `stratify` parameter and :class:`model\_selection.StratifiedShuffleSplit` may lead to slightly different results. 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.) Changelog --------- .. 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. - \|API\| The option for using the squared error via ``loss`` and ``criterion`` parameters was made more consistent. The preferred way is by setting the value to `"squared\_error"`. Old option names are still valid, produce the same models, but are deprecated and will be removed in version 1.2. :pr:`19310` by :user:`Christian Lorentzen `. - For :class:`ensemble.ExtraTreesRegressor`, `criterion="mse"` is deprecated, use `"squared\_error"` instead which is now the default. - For :class:`ensemble.GradientBoostingRegressor`, `loss="ls"` is deprecated, use `"squared\_error"` instead which is now the default. - For :class:`ensemble.RandomForestRegressor`, `criterion="mse"` is deprecated, use `"squared\_error"` instead which is now the default. - For :class:`ensemble.HistGradientBoostingRegressor`, `loss="least\_squares"` is deprecated, use `"squared\_error"` instead which is now the default. - For :class:`linear\_model.RANSACRegressor`, `loss="squared\_loss"` is deprecated, use `"squared\_error"` instead. - For :class:`linear\_model.SGDRegressor`, `loss="squared\_loss"` is deprecated, use `"squared\_error"` instead which is now the default. - For :class:`tree.DecisionTreeRegressor`, `criterion="mse"` is deprecated, use `"squared\_error"` instead which is now the default. - For :class:`tree.ExtraTreeRegressor`, `criterion="mse"` is deprecated, use `"squared\_error"` instead which is now the default. - \|API\| The option for using the absolute error via ``loss`` and ``criterion`` parameters was made more consistent. The preferred way is by setting the value to `"absolute\_error"`. Old option names are still valid, produce the same models, but are deprecated and will be removed in version 1.2. :pr:`19733` by :user:`Christian Lorentzen `. - For :class:`ensemble.ExtraTreesRegressor`, `criterion="mae"` is deprecated, use `"absolute\_error"` instead. - For :class:`ensemble.GradientBoostingRegressor`, `loss="lad"` is deprecated, use `"absolute\_error"` instead. - For :class:`ensemble.RandomForestRegressor`, `criterion="mae"` is deprecated, use `"absolute\_error"` instead. - For :class:`ensemble.HistGradientBoostingRegressor`, `loss="least\_absolute\_deviation"` is deprecated, use `"absolute\_error"` instead. - For :class:`linear\_model.RANSACRegressor`, `loss="absolute\_loss"` is deprecated, use `"absolute\_error"` instead which is now the default. - For :class:`tree.DecisionTreeRegressor`, `criterion="mae"` is deprecated, use `"absolute\_error"` instead. - For :class:`tree.ExtraTreeRegressor`, `criterion="mae"` is deprecated, use `"absolute\_error"` instead. - \|API\| `np.matrix` usage is deprecated in 1.0 and will raise a `TypeError` in 1.2. :pr:`20165` by `Thomas Fan`\_. - \|API\| :term:`get\_feature\_names\_out` has been added to the transformer API to get the names of the output features. `get\_feature\_names` has in turn been deprecated. :pr:`18444` by `Thomas Fan`\_. - \|API\| All estimators store `feature\_names\_in\_` when fitted on pandas Dataframes. These feature names are compared	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.07062652707099915, -0.06282057613134384, 0.06773081421852112, 0.023189229890704155, 0.0474943108856678, -0.07509035617113113, -0.0479295589029789, 0.019676124677062035, -0.0030607772059738636, 0.0024073782842606306, 0.08402291685342789, 0.0011740768095478415, 0.011850297451019287, -0.07...	0.091204
in 1.2. :pr:`20165` by `Thomas Fan`\_. - \|API\| :term:`get\_feature\_names\_out` has been added to the transformer API to get the names of the output features. `get\_feature\_names` has in turn been deprecated. :pr:`18444` by `Thomas Fan`\_. - \|API\| All estimators store `feature\_names\_in\_` when fitted on pandas Dataframes. These feature names are compared to names seen in non-`fit` methods, e.g. `transform` and will raise a `FutureWarning` if they are not consistent, see also :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_1\_0\_0.py`. These ``FutureWarning`` s will become ``ValueError`` s in 1.2. :pr:`18010` by `Thomas Fan`\_. :mod:`sklearn.base` ................... - \|Fix\| :func:`config\_context` is now threadsafe. :pr:`18736` by `Thomas Fan`\_. :mod:`sklearn.calibration` .......................... - \|Feature\| :func:`calibration.CalibrationDisplay` added to plot calibration curves. :pr:`17443` by :user:`Lucy Liu `. - \|Fix\| The ``predict`` and ``predict\_proba`` methods of :class:`calibration.CalibratedClassifierCV` can now properly be used on prefitted pipelines. :pr:`19641` by :user:`Alek Lefebvre `. - \|Fix\| Fixed an error when using a :class:`ensemble.VotingClassifier` as `base\_estimator` in :class:`calibration.CalibratedClassifierCV`. :pr:`20087` by :user:`Clément Fauchereau `. :mod:`sklearn.cluster` ...................... - \|Efficiency\| The ``"k-means++"`` initialization of :class:`cluster.KMeans` and :class:`cluster.MiniBatchKMeans` is now faster, especially in multicore settings. :pr:`19002` by :user:`Jon Crall ` and :user:`Jérémie du Boisberranger `. - \|Efficiency\| :class:`cluster.KMeans` with `algorithm='elkan'` is now faster in multicore settings. :pr:`19052` by :user:`Yusuke Nagasaka `. - \|Efficiency\| :class:`cluster.MiniBatchKMeans` is now faster in multicore settings. :pr:`17622` by :user:`Jérémie du Boisberranger `. - \|Efficiency\| :class:`cluster.OPTICS` can now cache the output of the computation of the tree, using the `memory` parameter. :pr:`19024` by :user:`Frankie Robertson `. - \|Enhancement\| The `predict` and `fit\_predict` methods of :class:`cluster.AffinityPropagation` now accept sparse data type for input data. :pr:`20117` by :user:`Venkatachalam Natchiappan ` - \|Fix\| Fixed a bug in :class:`cluster.MiniBatchKMeans` where the sample weights were partially ignored when the input is sparse. :pr:`17622` by :user:`Jérémie du Boisberranger `. - \|Fix\| Improved convergence detection based on center change in :class:`cluster.MiniBatchKMeans` which was almost never achievable. :pr:`17622` by :user:`Jérémie du Boisberranger `. - \|FIX\| :class:`cluster.AgglomerativeClustering` now supports readonly memory-mapped datasets. :pr:`19883` by :user:`Julien Jerphanion `. - \|Fix\| :class:`cluster.AgglomerativeClustering` correctly connects components when connectivity and affinity are both precomputed and the number of connected components is greater than 1. :pr:`20597` by `Thomas Fan`\_. - \|Fix\| :class:`cluster.FeatureAgglomeration` does not accept a ``\\params`` kwarg in the ``fit`` function anymore, resulting in a more concise error message. :pr:`20899` by :user:`Adam Li `. - \|Fix\| Fixed a bug in :class:`cluster.KMeans`, ensuring reproducibility and equivalence between sparse and dense input. :pr:`20200` by :user:`Jérémie du Boisberranger `. - \|API\| :class:`cluster.Birch` attributes, `fit\_` and `partial\_fit\_`, are deprecated and will be removed in 1.2. :pr:`19297` by `Thomas Fan`\_. - \|API\| the default value for the `batch\_size` parameter of :class:`cluster.MiniBatchKMeans` was changed from 100 to 1024 due to efficiency reasons. The `n\_iter\_` attribute of :class:`cluster.MiniBatchKMeans` now reports the number of started epochs and the `n\_steps\_` attribute reports the number of mini batches processed. :pr:`17622` by :user:`Jérémie du Boisberranger `. - \|API\| :func:`cluster.spectral\_clustering` raises an improved error when passed a `np.matrix`. :pr:`20560` by `Thomas Fan`\_. :mod:`sklearn.compose` ...................... - \|Enhancement\| :class:`compose.ColumnTransformer` now records the output of each transformer in `output\_indices\_`. :pr:`18393` by :user:`Luca Bittarello `. - \|Enhancement\| :class:`compose.ColumnTransformer` now allows DataFrame input to have its columns appear in a changed order in `transform`. Further, columns that are dropped will not be required in transform, and additional columns will be ignored if `remainder='drop'`. :pr:`19263` by `Thomas Fan`\_. - \|Enhancement\| Adds `\\predict\_params` keyword argument to :meth:`compose.TransformedTargetRegressor.predict` that passes keyword argument to the regressor. :pr:`19244` by :user:`Ricardo `. - \|FIX\| `compose.ColumnTransformer.get\_feature\_names` supports non-string feature names returned by any of its transformers. However, note that ``get\_feature\_names`` is deprecated, use ``get\_feature\_names\_out`` instead. :pr:`18459` by :user:`Albert Villanova del Moral ` and :user:`Alonso Silva Allende `. - \|Fix\| :class:`compose.TransformedTargetRegressor` now takes nD targets with an adequate transformer. :pr:`18898` by	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.13158884644508362, -0.04038342088460922, 0.0032352900598198175, -0.007510959170758724, 0.10825354605913162, -0.07513105869293213, -0.025102924555540085, 0.026925668120384216, 0.010784734971821308, -0.035543616861104965, 0.07934227585792542, 0.0022135397884994745, 0.013995327055454254, -...	0.088278
by :user:`Ricardo `. - \|FIX\| `compose.ColumnTransformer.get\_feature\_names` supports non-string feature names returned by any of its transformers. However, note that ``get\_feature\_names`` is deprecated, use ``get\_feature\_names\_out`` instead. :pr:`18459` by :user:`Albert Villanova del Moral ` and :user:`Alonso Silva Allende `. - \|Fix\| :class:`compose.TransformedTargetRegressor` now takes nD targets with an adequate transformer. :pr:`18898` by :user:`Oras Phongpanagnam `. - \|API\| Adds `verbose\_feature\_names\_out` to :class:`compose.ColumnTransformer`. This flag controls the prefixing of feature names out in :term:`get\_feature\_names\_out`. :pr:`18444` and :pr:`21080` by `Thomas Fan`\_. :mod:`sklearn.covariance` ......................... - \|Fix\| Adds arrays check to :func:`covariance.ledoit\_wolf` and :func:`covariance.ledoit\_wolf\_shrinkage`. :pr:`20416` by :user:`Hugo Defois `. - \|API\| Deprecates the following keys in `cv\_results\_`: `'mean\_score'`, `'std\_score'`, and `'split(k)\_score'` in favor of `'mean\_test\_score'` `'std\_test\_score'`, and `'split(k)\_test\_score'`. :pr:`20583` by `Thomas Fan`\_. :mod:`sklearn.datasets` ....................... - \|Enhancement\| :func:`datasets.fetch\_openml` now supports categories with missing values when returning a pandas dataframe. :pr:`19365` by `Thomas Fan`\_ and :user:`Amanda Dsouza ` and :user:`EL-ATEIF Sara `. - \|Enhancement\| :func:`datasets.fetch\_kddcup99` raises a better message when the cached file is invalid. :pr:`19669` `Thomas Fan`\_. - \|Enhancement\| Replace usages of ``\_\_file\_\_`` related to resource file I/O with ``importlib.resources`` to avoid the assumption that these resource files (e.g. ``iris.csv``) already exist on a filesystem, and by extension to enable compatibility with tools such as ``PyOxidizer``. :pr:`20297` by :user:`Jack Liu `. - \|Fix\| Shorten data file names in the openml tests to better support installing on Windows and its default 260 character limit on file names. :pr:`20209` by `Thomas Fan`\_. - \|Fix\| :func:`datasets.fetch\_kddcup99` returns dataframes when `return\_X\_y=True` and `as\_frame=True`. :pr:`19011` by `Thomas Fan`\_. - \|API\| Deprecates `datasets.load\_boston` in 1.0 and it will be removed in 1.2. Alternative code snippets to load similar datasets are provided. Please report to the docstring of the function for details. :pr:`20729` by `Guillaume Lemaitre`\_. :mod:`sklearn.decomposition` ............................ - \|Enhancement\| added a new approximate solver (randomized SVD, available with `eigen\_solver='randomized'`) to :class:`decomposition.KernelPCA`. This significantly accelerates computation when the number of samples is much larger than the desired number of components. :pr:`12069` by :user:`Sylvain Marié `. - \|Fix\| Fixes incorrect multiple data-conversion warnings when clustering boolean data. :pr:`19046` by :user:`Surya Prakash `. - \|Fix\| Fixed :func:`decomposition.dict\_learning`, used by :class:`decomposition.DictionaryLearning`, to ensure determinism of the output. Achieved by flipping signs of the SVD output which is used to initialize the code. :pr:`18433` by :user:`Bruno Charron `. - \|Fix\| Fixed a bug in :class:`decomposition.MiniBatchDictionaryLearning`, :class:`decomposition.MiniBatchSparsePCA` and :func:`decomposition.dict\_learning\_online` where the update of the dictionary was incorrect. :pr:`19198` by :user:`Jérémie du Boisberranger `. - \|Fix\| Fixed a bug in :class:`decomposition.DictionaryLearning`, :class:`decomposition.SparsePCA`, :class:`decomposition.MiniBatchDictionaryLearning`, :class:`decomposition.MiniBatchSparsePCA`, :func:`decomposition.dict\_learning` and :func:`decomposition.dict\_learning\_online` where the restart of unused atoms during the dictionary update was not working as expected. :pr:`19198` by :user:`Jérémie du Boisberranger `. - \|API\| In :class:`decomposition.DictionaryLearning`, :class:`decomposition.MiniBatchDictionaryLearning`, :func:`decomposition.dict\_learning` and :func:`decomposition.dict\_learning\_online`, `transform\_alpha` will be equal to `alpha` instead of 1.0 by default starting from version 1.2 :pr:`19159` by :user:`Benoît Malézieux `. - \|API\| Rename variable names in :class:`decomposition.KernelPCA` to improve readability. `lambdas\_` and `alphas\_` are renamed to `eigenvalues\_` and `eigenvectors\_`, respectively. `lambdas\_` and `alphas\_` are deprecated and will be removed in 1.2. :pr:`19908` by :user:`Kei Ishikawa `. - \|API\| The `alpha` and `regularization` parameters of :class:`decomposition.NMF` and :func:`decomposition.non\_negative\_factorization` are deprecated and will be removed in 1.2. Use the new parameters `alpha\_W` and `alpha\_H` instead. :pr:`20512` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.dummy` .................... - \|API\| Attribute `n\_features\_in\_` in :class:`dummy.DummyRegressor` and :class:`dummy.DummyRegressor` is deprecated and will be removed in 1.2. :pr:`20960` by `Thomas Fan`\_. :mod:`sklearn.ensemble` ....................... - \|Enhancement\| :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and :class:`~sklearn.ensemble.HistGradientBoostingRegressor` take cgroups quotas into account when deciding the number of threads used by OpenMP. This avoids performance problems caused by over-subscription when using those classes in a docker container for instance. :pr:`20477` by `Thomas Fan`\_. - \|Enhancement\| :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and :class:`~sklearn.ensemble.HistGradientBoostingRegressor` are no longer experimental. They are now	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.09240522980690002, -0.008719922974705696, -0.03320000320672989, -0.004304881673306227, -0.06627379357814789, 0.009783338755369186, -0.0428425557911396, 0.04503011703491211, -0.05590583384037018, -0.05604468658566475, 0.05952557921409607, -0.08504008501768112, -0.013989834114909172, -0.0...	0.051708
:class:`~sklearn.ensemble.HistGradientBoostingClassifier` and :class:`~sklearn.ensemble.HistGradientBoostingRegressor` take cgroups quotas into account when deciding the number of threads used by OpenMP. This avoids performance problems caused by over-subscription when using those classes in a docker container for instance. :pr:`20477` by `Thomas Fan`\_. - \|Enhancement\| :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and :class:`~sklearn.ensemble.HistGradientBoostingRegressor` are no longer experimental. They are now considered stable and are subject to the same deprecation cycles as all other estimators. :pr:`19799` by `Nicolas Hug`\_. - \|Enhancement\| Improve the HTML rendering of the :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor`. :pr:`19564` by `Thomas Fan`\_. - \|Enhancement\| Added Poisson criterion to :class:`ensemble.RandomForestRegressor`. :pr:`19836` by :user:`Brian Sun `. - \|Fix\| Do not allow to compute out-of-bag (OOB) score in :class:`ensemble.RandomForestClassifier` and :class:`ensemble.ExtraTreesClassifier` with multiclass-multioutput target since scikit-learn does not provide any metric supporting this type of target. Additional private refactoring was performed. :pr:`19162` by :user:`Guillaume Lemaitre `. - \|Fix\| Improve numerical precision for weights boosting in :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` to avoid underflows. :pr:`10096` by :user:`Fenil Suchak `. - \|Fix\| Fixed the range of the argument ``max\_samples`` to be ``(0.0, 1.0]`` in :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, where `max\_samples=1.0` is interpreted as using all `n\_samples` for bootstrapping. :pr:`20159` by :user:`murata-yu`. - \|Fix\| Fixed a bug in :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` where the `sample\_weight` parameter got overwritten during `fit`. :pr:`20534` by :user:`Guillaume Lemaitre `. - \|API\| Removes `tol=None` option in :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`. Please use `tol=0` for the same behavior. :pr:`19296` by `Thomas Fan`\_. :mod:`sklearn.feature\_extraction` ................................. - \|Fix\| Fixed a bug in :class:`feature\_extraction.text.HashingVectorizer` where some input strings would result in negative indices in the transformed data. :pr:`19035` by :user:`Liu Yu `. - \|Fix\| Fixed a bug in :class:`feature\_extraction.DictVectorizer` by raising an error with unsupported value type. :pr:`19520` by :user:`Jeff Zhao `. - \|Fix\| Fixed a bug in :func:`feature\_extraction.image.img\_to\_graph` and :func:`feature\_extraction.image.grid\_to\_graph` where singleton connected components were not handled properly, resulting in a wrong vertex indexing. :pr:`18964` by `Bertrand Thirion`\_. - \|Fix\| Raise a warning in :class:`feature\_extraction.text.CountVectorizer` with `lowercase=True` when there are vocabulary entries with uppercase characters to avoid silent misses in the resulting feature vectors. :pr:`19401` by :user:`Zito Relova ` :mod:`sklearn.feature\_selection` ................................ - \|Feature\| :func:`feature\_selection.r\_regression` computes Pearson's R correlation coefficients between the features and the target. :pr:`17169` by :user:`Dmytro Lituiev ` and :user:`Julien Jerphanion `. - \|Enhancement\| :func:`feature\_selection.RFE.fit` accepts additional estimator parameters that are passed directly to the estimator's `fit` method. :pr:`20380` by :user:`Iván Pulido `, :user:`Felipe Bidu `, :user:`Gil Rutter `, and :user:`Adrin Jalali `. - \|FIX\| Fix a bug in :func:`isotonic.isotonic\_regression` where the `sample\_weight` passed by a user were overwritten during ``fit``. :pr:`20515` by :user:`Carsten Allefeld `. - \|Fix\| Change :func:`feature\_selection.SequentialFeatureSelector` to allow for unsupervised modelling so that the `fit` signature need not do any `y` validation and allow for `y=None`. :pr:`19568` by :user:`Shyam Desai `. - \|API\| Raises an error in :class:`feature\_selection.VarianceThreshold` when the variance threshold is negative. :pr:`20207` by :user:`Tomohiro Endo ` - \|API\| Deprecates `grid\_scores\_` in favor of split scores in `cv\_results\_` in :class:`feature\_selection.RFECV`. `grid\_scores\_` will be removed in version 1.2. :pr:`20161` by :user:`Shuhei Kayawari ` and :user:`arka204`. :mod:`sklearn.inspection` ......................... - \|Enhancement\| Add `max\_samples` parameter in :func:`inspection.permutation\_importance`. It enables to draw a subset of the samples to compute the permutation importance. This is useful to keep the method tractable when evaluating feature importance on large datasets. :pr:`20431` by :user:`Oliver Pfaffel `. - \|Enhancement\| Add kwargs to format ICE and PD lines separately in partial dependence plots `inspection.plot\_partial\_dependence` and :meth:`inspection.PartialDependenceDisplay.plot`. :pr:`19428` by :user:`Mehdi Hamoumi `. - \|Fix\| Allow multiple scorers input to :func:`inspection.permutation\_importance`. :pr:`19411` by :user:`Simona Maggio `. - \|API\| :class:`inspection.PartialDependenceDisplay` exposes a class method: :func:`~inspection.PartialDependenceDisplay.from\_estimator`. `inspection.plot\_partial\_dependence` is deprecated in favor of the class method and will be removed in 1.2. :pr:`20959` by `Thomas Fan`\_. :mod:`sklearn.kernel\_approximation` ................................... - \|Fix\| Fix a bug in :class:`kernel\_approximation.Nystroem` where the	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.101547472178936, -0.054521311074495316, -0.03342127427458763, 0.003556176321581006, 0.08442414551973343, -0.028843937441706657, -0.0643216222524643, -0.04141389951109886, -0.0031886757351458073, 0.021353259682655334, -0.0561077818274498, -0.03659803792834282, 0.022689910605549812, -0.11...	0.013853
\|Fix\| Allow multiple scorers input to :func:`inspection.permutation\_importance`. :pr:`19411` by :user:`Simona Maggio `. - \|API\| :class:`inspection.PartialDependenceDisplay` exposes a class method: :func:`~inspection.PartialDependenceDisplay.from\_estimator`. `inspection.plot\_partial\_dependence` is deprecated in favor of the class method and will be removed in 1.2. :pr:`20959` by `Thomas Fan`\_. :mod:`sklearn.kernel\_approximation` ................................... - \|Fix\| Fix a bug in :class:`kernel\_approximation.Nystroem` where the attribute `component\_indices\_` did not correspond to the subset of sample indices used to generate the approximated kernel. :pr:`20554` by :user:`Xiangyin Kong `. :mod:`sklearn.linear\_model` ........................... - \|MajorFeature\| Added :class:`linear\_model.QuantileRegressor` which implements linear quantile regression with L1 penalty. :pr:`9978` by :user:`David Dale ` and :user:`Christian Lorentzen `. - \|Feature\| The new :class:`linear\_model.SGDOneClassSVM` provides an SGD implementation of the linear One-Class SVM. Combined with kernel approximation techniques, this implementation approximates the solution of a kernelized One Class SVM while benefiting from a linear complexity in the number of samples. :pr:`10027` by :user:`Albert Thomas `. - \|Feature\| Added `sample\_weight` parameter to :class:`linear\_model.LassoCV` and :class:`linear\_model.ElasticNetCV`. :pr:`16449` by :user:`Christian Lorentzen `. - \|Feature\| Added new solver `lbfgs` (available with `solver="lbfgs"`) and `positive` argument to :class:`linear\_model.Ridge`. When `positive` is set to `True`, forces the coefficients to be positive (only supported by `lbfgs`). :pr:`20231` by :user:`Toshihiro Nakae `. - \|Efficiency\| The implementation of :class:`linear\_model.LogisticRegression` has been optimised for dense matrices when using `solver='newton-cg'` and `multi\_class!='multinomial'`. :pr:`19571` by :user:`Julien Jerphanion `. - \|Enhancement\| `fit` method preserves dtype for numpy.float32 in :class:`linear\_model.Lars`, :class:`linear\_model.LassoLars`, :class:`linear\_model.LassoLars`, :class:`linear\_model.LarsCV` and :class:`linear\_model.LassoLarsCV`. :pr:`20155` by :user:`Takeshi Oura `. - \|Enhancement\| Validate user-supplied gram matrix passed to linear models via the `precompute` argument. :pr:`19004` by :user:`Adam Midvidy `. - \|Fix\| :meth:`linear\_model.ElasticNet.fit` no longer modifies `sample\_weight` in place. :pr:`19055` by `Thomas Fan`\_. - \|Fix\| :class:`linear\_model.Lasso` and :class:`linear\_model.ElasticNet` no longer have a `dual\_gap\_` not corresponding to their objective. :pr:`19172` by :user:`Mathurin Massias ` - \|Fix\| `sample\_weight` are now fully taken into account in linear models when `normalize=True` for both feature centering and feature scaling. :pr:`19426` by :user:`Alexandre Gramfort ` and :user:`Maria Telenczuk `. - \|Fix\| Points with residuals equal to ``residual\_threshold`` are now considered as inliers for :class:`linear\_model.RANSACRegressor`. This allows fitting a model perfectly on some datasets when `residual\_threshold=0`. :pr:`19499` by :user:`Gregory Strubel `. - \|Fix\| Sample weight invariance for :class:`linear\_model.Ridge` was fixed in :pr:`19616` by :user:`Oliver Grisel ` and :user:`Christian Lorentzen `. - \|Fix\| The dictionary `params` in :func:`linear\_model.enet\_path` and :func:`linear\_model.lasso\_path` should only contain parameter of the coordinate descent solver. Otherwise, an error will be raised. :pr:`19391` by :user:`Shao Yang Hong `. - \|API\| Raise a warning in :class:`linear\_model.RANSACRegressor` that from version 1.2, `min\_samples` need to be set explicitly for models other than :class:`linear\_model.LinearRegression`. :pr:`19390` by :user:`Shao Yang Hong `. - \|API\|: The parameter ``normalize`` of :class:`linear\_model.LinearRegression` is deprecated and will be removed in 1.2. Motivation for this deprecation: ``normalize`` parameter did not take any effect if ``fit\_intercept`` was set to False and therefore was deemed confusing. The behavior of the deprecated ``LinearModel(normalize=True)`` can be reproduced with a :class:`~sklearn.pipeline.Pipeline` with ``LinearModel`` (where ``LinearModel`` is :class:`~linear\_model.LinearRegression`, :class:`~linear\_model.Ridge`, :class:`~linear\_model.RidgeClassifier`, :class:`~linear\_model.RidgeCV` or :class:`~linear\_model.RidgeClassifierCV`) as follows: ``make\_pipeline(StandardScaler(with\_mean=False), LinearModel())``. The ``normalize`` parameter in :class:`~linear\_model.LinearRegression` was deprecated in :pr:`17743` by :user:`Maria Telenczuk ` and :user:`Alexandre Gramfort `. Same for :class:`~linear\_model.Ridge`, :class:`~linear\_model.RidgeClassifier`, :class:`~linear\_model.RidgeCV`, and :class:`~linear\_model.RidgeClassifierCV`, in: :pr:`17772` by :user:`Maria Telenczuk ` and :user:`Alexandre Gramfort `. Same for :class:`~linear\_model.BayesianRidge`, :class:`~linear\_model.ARDRegression` in: :pr:`17746` by :user:`Maria Telenczuk `. Same for :class:`~linear\_model.Lasso`, :class:`~linear\_model.LassoCV`, :class:`~linear\_model.ElasticNet`, :class:`~linear\_model.ElasticNetCV`, :class:`~linear\_model.MultiTaskLasso`, :class:`~linear\_model.MultiTaskLassoCV`, :class:`~linear\_model.MultiTaskElasticNet`, :class:`~linear\_model.MultiTaskElasticNetCV`, in: :pr:`17785` by :user:`Maria Telenczuk ` and :user:`Alexandre Gramfort `. - \|API\| The ``normalize`` parameter of :class:`~linear\_model.OrthogonalMatchingPursuit` and :class:`~linear\_model.OrthogonalMatchingPursuitCV` will default to False in 1.2 and will be removed in 1.4. :pr:`17750` by :user:`Maria Telenczuk ` and :user:`Alexandre Gramfort `. Same for :class:`~linear\_model.Lars` :class:`~linear\_model.LarsCV` :class:`~linear\_model.LassoLars` :class:`~linear\_model.LassoLarsCV` :class:`~linear\_model.LassoLarsIC`, in :pr:`17769` by :user:`Maria Telenczuk ` and :user:`Alexandre Gramfort `. - \|API\| Keyword	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.029556304216384888, 0.02609175443649292, -0.019085930660367012, 0.015388785861432552, 0.042697690427303314, -0.019678620621562004, 0.051617689430713654, 0.05076325312256813, -0.08348012715578079, -0.011018221266567707, 0.05701743811368942, -0.0038841867353767157, -0.014243390411138535, ...	0.15343
- \|API\| The ``normalize`` parameter of :class:`~linear\_model.OrthogonalMatchingPursuit` and :class:`~linear\_model.OrthogonalMatchingPursuitCV` will default to False in 1.2 and will be removed in 1.4. :pr:`17750` by :user:`Maria Telenczuk ` and :user:`Alexandre Gramfort `. Same for :class:`~linear\_model.Lars` :class:`~linear\_model.LarsCV` :class:`~linear\_model.LassoLars` :class:`~linear\_model.LassoLarsCV` :class:`~linear\_model.LassoLarsIC`, in :pr:`17769` by :user:`Maria Telenczuk ` and :user:`Alexandre Gramfort `. - \|API\| Keyword validation has moved from `\_\_init\_\_` and `set\_params` to `fit` for the following estimators conforming to scikit-learn's conventions: :class:`~linear\_model.SGDClassifier`, :class:`~linear\_model.SGDRegressor`, :class:`~linear\_model.SGDOneClassSVM`, :class:`~linear\_model.PassiveAggressiveClassifier`, and :class:`~linear\_model.PassiveAggressiveRegressor`. :pr:`20683` by `Guillaume Lemaitre`\_. :mod:`sklearn.manifold` ....................... - \|Enhancement\| Implement `'auto'` heuristic for the `learning\_rate` in :class:`manifold.TSNE`. It will become default in 1.2. The default initialization will change to `pca` in 1.2. PCA initialization will be scaled to have standard deviation 1e-4 in 1.2. :pr:`19491` by :user:`Dmitry Kobak `. - \|Fix\| Change numerical precision to prevent underflow issues during affinity matrix computation for :class:`manifold.TSNE`. :pr:`19472` by :user:`Dmitry Kobak `. - \|Fix\| :class:`manifold.Isomap` now uses `scipy.sparse.csgraph.shortest\_path` to compute the graph shortest path. It also connects disconnected components of the neighbors graph along some minimum distance pairs, instead of changing every infinite distances to zero. :pr:`20531` by `Roman Yurchak`\_ and `Tom Dupre la Tour`\_. - \|Fix\| Decrease the numerical default tolerance in the lobpcg call in :func:`manifold.spectral\_embedding` to prevent numerical instability. :pr:`21194` by :user:`Andrew Knyazev `. :mod:`sklearn.metrics` ...................... - \|Feature\| :func:`metrics.mean\_pinball\_loss` exposes the pinball loss for quantile regression. :pr:`19415` by :user:`Xavier Dupré ` and :user:`Oliver Grisel `. - \|Feature\| :func:`metrics.d2\_tweedie\_score` calculates the D^2 regression score for Tweedie deviances with power parameter ``power``. This is a generalization of the `r2\_score` and can be interpreted as percentage of Tweedie deviance explained. :pr:`17036` by :user:`Christian Lorentzen `. - \|Feature\| :func:`metrics.mean\_squared\_log\_error` now supports `squared=False`. :pr:`20326` by :user:`Uttam kumar `. - \|Efficiency\| Improved speed of :func:`metrics.confusion\_matrix` when labels are integral. :pr:`9843` by :user:`Jon Crall `. - \|Enhancement\| A fix to raise an error in :func:`metrics.hinge\_loss` when ``pred\_decision`` is 1d whereas it is a multiclass classification or when ``pred\_decision`` parameter is not consistent with the ``labels`` parameter. :pr:`19643` by :user:`Pierre Attard `. - \|Fix\| :meth:`metrics.ConfusionMatrixDisplay.plot` uses the correct max for colormap. :pr:`19784` by `Thomas Fan`\_. - \|Fix\| Samples with zero `sample\_weight` values do not affect the results from :func:`metrics.det\_curve`, :func:`metrics.precision\_recall\_curve` and :func:`metrics.roc\_curve`. :pr:`18328` by :user:`Albert Villanova del Moral ` and :user:`Alonso Silva Allende `. - \|Fix\| avoid overflow in :func:`metrics.adjusted\_rand\_score` with large amount of data. :pr:`20312` by :user:`Divyanshu Deoli `. - \|API\| :class:`metrics.ConfusionMatrixDisplay` exposes two class methods :func:`~metrics.ConfusionMatrixDisplay.from\_estimator` and :func:`~metrics.ConfusionMatrixDisplay.from\_predictions` allowing to create a confusion matrix plot using an estimator or the predictions. `metrics.plot\_confusion\_matrix` is deprecated in favor of these two class methods and will be removed in 1.2. :pr:`18543` by `Guillaume Lemaitre`\_. - \|API\| :class:`metrics.PrecisionRecallDisplay` exposes two class methods :func:`~metrics.PrecisionRecallDisplay.from\_estimator` and :func:`~metrics.PrecisionRecallDisplay.from\_predictions` allowing to create a precision-recall curve using an estimator or the predictions. `metrics.plot\_precision\_recall\_curve` is deprecated in favor of these two class methods and will be removed in 1.2. :pr:`20552` by `Guillaume Lemaitre`\_. - \|API\| :class:`metrics.DetCurveDisplay` exposes two class methods :func:`~metrics.DetCurveDisplay.from\_estimator` and :func:`~metrics.DetCurveDisplay.from\_predictions` allowing to create a confusion matrix plot using an estimator or the predictions. `metrics.plot\_det\_curve` is deprecated in favor of these two class methods and will be removed in 1.2. :pr:`19278` by `Guillaume Lemaitre`\_. :mod:`sklearn.mixture` ...................... - \|Fix\| Ensure that the best parameters are set appropriately in the case of divergency for :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture`. :pr:`20030` by :user:`Tingshan Liu ` and :user:`Benjamin Pedigo `. :mod:`sklearn.model\_selection` .............................. - \|Feature\| added :class:`model\_selection.StratifiedGroupKFold`, that combines :class:`model\_selection.StratifiedKFold` and :class:`model\_selection.GroupKFold`, providing an ability to split data preserving the distribution of classes in each split while keeping each group within a single split. :pr:`18649` by :user:`Leandro Hermida ` and :user:`Rodion Martynov `. - \|Enhancement\| warn only once in the main process for per-split fit failures in cross-validation. :pr:`20619`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.09903325885534286, -0.05393008887767792, -0.07403881847858429, -0.04397507384419441, 0.008992116898298264, -0.029104122892022133, -0.0058836545795202255, 0.030500128865242004, -0.05865880846977234, 0.03201889246702194, 0.10258999466896057, -0.006189190316945314, -0.005270888097584248, -...	0.05838
:class:`model\_selection.StratifiedKFold` and :class:`model\_selection.GroupKFold`, providing an ability to split data preserving the distribution of classes in each split while keeping each group within a single split. :pr:`18649` by :user:`Leandro Hermida ` and :user:`Rodion Martynov `. - \|Enhancement\| warn only once in the main process for per-split fit failures in cross-validation. :pr:`20619` by :user:`Loïc Estève ` - \|Enhancement\| The `model\_selection.BaseShuffleSplit` base class is now public. :pr:`20056` by :user:`pabloduque0`. - \|Fix\| Avoid premature overflow in :func:`model\_selection.train\_test\_split`. :pr:`20904` by :user:`Tomasz Jakubek `. :mod:`sklearn.naive\_bayes` .......................... - \|Fix\| The `fit` and `partial\_fit` methods of the discrete naive Bayes classifiers (:class:`naive\_bayes.BernoulliNB`, :class:`naive\_bayes.CategoricalNB`, :class:`naive\_bayes.ComplementNB`, and :class:`naive\_bayes.MultinomialNB`) now correctly handle the degenerate case of a single class in the training set. :pr:`18925` by :user:`David Poznik `. - \|API\| The attribute ``sigma\_`` is now deprecated in :class:`naive\_bayes.GaussianNB` and will be removed in 1.2. Use ``var\_`` instead. :pr:`18842` by :user:`Hong Shao Yang `. :mod:`sklearn.neighbors` ........................ - \|Enhancement\| The creation of :class:`neighbors.KDTree` and :class:`neighbors.BallTree` has been improved for their worst-cases time complexity from :math:`\mathcal{O}(n^2)` to :math:`\mathcal{O}(n)`. :pr:`19473` by :user:`jiefangxuanyan ` and :user:`Julien Jerphanion `. - \|FIX\| `neighbors.DistanceMetric` subclasses now support readonly memory-mapped datasets. :pr:`19883` by :user:`Julien Jerphanion `. - \|FIX\| :class:`neighbors.NearestNeighbors`, :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor` and :class:`neighbors.RadiusNeighborsRegressor` do not validate `weights` in `\_\_init\_\_` and validate `weights` in `fit` instead. :pr:`20072` by :user:`Juan Carlos Alfaro Jiménez `. - \|API\| The parameter `kwargs` of :class:`neighbors.RadiusNeighborsClassifier` is deprecated and will be removed in 1.2. :pr:`20842` by :user:`Juan Martín Loyola `. :mod:`sklearn.neural\_network` ............................. - \|Fix\| :class:`neural\_network.MLPClassifier` and :class:`neural\_network.MLPRegressor` now correctly support continued training when loading from a pickled file. :pr:`19631` by `Thomas Fan`\_. :mod:`sklearn.pipeline` ....................... - \|API\| The `predict\_proba` and `predict\_log\_proba` methods of the :class:`pipeline.Pipeline` now support passing prediction kwargs to the final estimator. :pr:`19790` by :user:`Christopher Flynn `. :mod:`sklearn.preprocessing` ............................ - \|Feature\| The new :class:`preprocessing.SplineTransformer` is a feature preprocessing tool for the generation of B-splines, parametrized by the polynomial ``degree`` of the splines, number of knots ``n\_knots`` and knot positioning strategy ``knots``. :pr:`18368` by :user:`Christian Lorentzen `. :class:`preprocessing.SplineTransformer` also supports periodic splines via the ``extrapolation`` argument. :pr:`19483` by :user:`Malte Londschien `. :class:`preprocessing.SplineTransformer` supports sample weights for knot position strategy ``"quantile"``. :pr:`20526` by :user:`Malte Londschien `. - \|Feature\| :class:`preprocessing.OrdinalEncoder` supports passing through missing values by default. :pr:`19069` by `Thomas Fan`\_. - \|Feature\| :class:`preprocessing.OneHotEncoder` now supports `handle\_unknown='ignore'` and dropping categories. :pr:`19041` by `Thomas Fan`\_. - \|Feature\| :class:`preprocessing.PolynomialFeatures` now supports passing a tuple to `degree`, i.e. `degree=(min\_degree, max\_degree)`. :pr:`20250` by :user:`Christian Lorentzen `. - \|Efficiency\| :class:`preprocessing.StandardScaler` is faster and more memory efficient. :pr:`20652` by `Thomas Fan`\_. - \|Efficiency\| Changed ``algorithm`` argument for :class:`cluster.KMeans` in :class:`preprocessing.KBinsDiscretizer` from ``auto`` to ``full``. :pr:`19934` by :user:`Hleb Levitski `. - \|Efficiency\| The implementation of `fit` for :class:`preprocessing.PolynomialFeatures` transformer is now faster. This is especially noticeable on large sparse input. :pr:`19734` by :user:`Fred Robinson `. - \|Fix\| The :func:`preprocessing.StandardScaler.inverse\_transform` method now raises error when the input data is 1D. :pr:`19752` by :user:`Zhehao Liu `. - \|Fix\| :func:`preprocessing.scale`, :class:`preprocessing.StandardScaler` and similar scalers detect near-constant features to avoid scaling them to very large values. This problem happens in particular when using a scaler on sparse data with a constant column with sample weights, in which case centering is typically disabled. :pr:`19527` by :user:`Oliver Grisel ` and :user:`Maria Telenczuk ` and :pr:`19788` by :user:`Jérémie du Boisberranger `. - \|Fix\| :meth:`preprocessing.StandardScaler.inverse\_transform` now correctly handles integer dtypes. :pr:`19356` by :user:`makoeppel`. - \|Fix\| :meth:`preprocessing.OrdinalEncoder.inverse\_transform` is not supporting sparse matrix and raises the appropriate error message. :pr:`19879` by :user:`Guillaume Lemaitre `. - \|Fix\| The `fit` method of :class:`preprocessing.OrdinalEncoder` will not raise error when `handle\_unknown='ignore'` and unknown categories are given to `fit`. :pr:`19906` by :user:`Zhehao Liu `. - \|Fix\| Fix a regression in :class:`preprocessing.OrdinalEncoder` where large Python numeric would raise an error due	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.06977121531963348, -0.021861806511878967, 0.00983670074492693, 0.02536117471754551, 0.06179814040660858, -0.0064371442422270775, -0.005642094183713198, 0.05696171522140503, -0.10666247457265854, -0.0020937768276780844, 0.0718022957444191, -0.07369130849838257, 0.0047830515541136265, -0....	0.06532
the appropriate error message. :pr:`19879` by :user:`Guillaume Lemaitre `. - \|Fix\| The `fit` method of :class:`preprocessing.OrdinalEncoder` will not raise error when `handle\_unknown='ignore'` and unknown categories are given to `fit`. :pr:`19906` by :user:`Zhehao Liu `. - \|Fix\| Fix a regression in :class:`preprocessing.OrdinalEncoder` where large Python numeric would raise an error due to overflow when casted to C type (`np.float64` or `np.int64`). :pr:`20727` by `Guillaume Lemaitre`\_. - \|Fix\| :class:`preprocessing.FunctionTransformer` does not set `n\_features\_in\_` based on the input to `inverse\_transform`. :pr:`20961` by `Thomas Fan`\_. - \|API\| The `n\_input\_features\_` attribute of :class:`preprocessing.PolynomialFeatures` is deprecated in favor of `n\_features\_in\_` and will be removed in 1.2. :pr:`20240` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.svm` ................... - \|API\| The parameter `\\params` of :func:`svm.OneClassSVM.fit` is deprecated and will be removed in 1.2. :pr:`20843` by :user:`Juan Martín Loyola `. :mod:`sklearn.tree` ................... - \|Enhancement\| Add `fontname` argument in :func:`tree.export\_graphviz` for non-English characters. :pr:`18959` by :user:`Zero ` and :user:`wstates `. - \|Fix\| Improves compatibility of :func:`tree.plot\_tree` with high DPI screens. :pr:`20023` by `Thomas Fan`\_. - \|Fix\| Fixed a bug in :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor` where a node could be split whereas it should not have been due to incorrect handling of rounding errors. :pr:`19336` by :user:`Jérémie du Boisberranger `. - \|API\| The `n\_features\_` attribute of :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier` and :class:`tree.ExtraTreeRegressor` is deprecated in favor of `n\_features\_in\_` and will be removed in 1.2. :pr:`20272` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.utils` .................... - \|Enhancement\| Deprecated the default value of the `random\_state=0` in :func:`~sklearn.utils.extmath.randomized\_svd`. Starting in 1.2, the default value of `random\_state` will be set to `None`. :pr:`19459` by :user:`Cindy Bezuidenhout ` and :user:`Clifford Akai-Nettey`. - \|Enhancement\| Added helper decorator :func:`utils.metaestimators.available\_if` to provide flexibility in metaestimators making methods available or unavailable on the basis of state, in a more readable way. :pr:`19948` by `Joel Nothman`\_. - \|Enhancement\| :func:`utils.validation.check\_is\_fitted` now uses ``\_\_sklearn\_is\_fitted\_\_`` if available, instead of checking for attributes ending with an underscore. This also makes :class:`pipeline.Pipeline` and :class:`preprocessing.FunctionTransformer` pass ``check\_is\_fitted(estimator)``. :pr:`20657` by `Adrin Jalali`\_. - \|Fix\| Fixed a bug in :func:`utils.sparsefuncs.mean\_variance\_axis` where the precision of the computed variance was very poor when the real variance is exactly zero. :pr:`19766` by :user:`Jérémie du Boisberranger `. - \|Fix\| The docstrings of properties that are decorated with :func:`utils.deprecated` are now properly wrapped. :pr:`20385` by `Thomas Fan`\_. - \|Fix\| `utils.stats.\_weighted\_percentile` now correctly ignores zero-weighted observations smaller than the smallest observation with positive weight for ``percentile=0``. Affected classes are :class:`dummy.DummyRegressor` for ``quantile=0`` and `ensemble.HuberLossFunction` and `ensemble.HuberLossFunction` for ``alpha=0``. :pr:`20528` by :user:`Malte Londschien `. - \|Fix\| :func:`utils.\_safe\_indexing` explicitly takes a dataframe copy when integer indices are provided avoiding to raise a warning from Pandas. This warning was previously raised in resampling utilities and functions using those utilities (e.g. :func:`model\_selection.train\_test\_split`, :func:`model\_selection.cross\_validate`, :func:`model\_selection.cross\_val\_score`, :func:`model\_selection.cross\_val\_predict`). :pr:`20673` by :user:`Joris Van den Bossche `. - \|Fix\| Fix a regression in `utils.is\_scalar\_nan` where large Python numbers would raise an error due to overflow in C types (`np.float64` or `np.int64`). :pr:`20727` by `Guillaume Lemaitre`\_. - \|Fix\| Support for `np.matrix` is deprecated in :func:`~sklearn.utils.check\_array` in 1.0 and will raise a `TypeError` in 1.2. :pr:`20165` by `Thomas Fan`\_. - \|API\| `utils.\_testing.assert\_warns` and `utils.\_testing.assert\_warns\_message` are deprecated in 1.0 and will be removed in 1.2. Used `pytest.warns` context manager instead. Note that these functions were not documented and part from the public API. :pr:`20521` by :user:`Olivier Grisel `. - \|API\| Fixed several bugs in `utils.graph.graph\_shortest\_path`, which is now deprecated. Use `scipy.sparse.csgraph.shortest\_path` instead. :pr:`20531` by `Tom Dupre la Tour`\_. .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.24, including: Abdulelah S. Al Mesfer, Abhinav Gupta, Adam J. Stewart, Adam Li, Adam Midvidy, Adrian Garcia Badaracco, Adrian Sadłocha, Adrin Jalali, Agamemnon Krasoulis,	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.08803790807723999, 0.029040630906820297, 0.004700936377048492, 0.005209602881222963, -0.007931379601359367, -0.010145127773284912, -0.05621955171227455, 0.02458115667104721, -0.09997335821390152, -0.01904786005616188, 0.04694207385182381, -0.06213998422026634, 0.03863833099603653, -0.10...	0.021295
`Tom Dupre la Tour`\_. .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.24, including: Abdulelah S. Al Mesfer, Abhinav Gupta, Adam J. Stewart, Adam Li, Adam Midvidy, Adrian Garcia Badaracco, Adrian Sadłocha, Adrin Jalali, Agamemnon Krasoulis, Alberto Rubiales, Albert Thomas, Albert Villanova del Moral, Alek Lefebvre, Alessia Marcolini, Alexandr Fonari, Alihan Zihna, Aline Ribeiro de Almeida, Amanda, Amanda Dsouza, Amol Deshmukh, Ana Pessoa, Anavelyz, Andreas Mueller, Andrew Delong, Ashish, Ashvith Shetty, Atsushi Nukariya, Aurélien Geron, Avi Gupta, Ayush Singh, baam, BaptBillard, Benjamin Pedigo, Bertrand Thirion, Bharat Raghunathan, bmalezieux, Brian Rice, Brian Sun, Bruno Charron, Bryan Chen, bumblebee, caherrera-meli, Carsten Allefeld, CeeThinwa, Chiara Marmo, chrissobel, Christian Lorentzen, Christopher Yeh, Chuliang Xiao, Clément Fauchereau, cliffordEmmanuel, Conner Shen, Connor Tann, David Dale, David Katz, David Poznik, Dimitri Papadopoulos Orfanos, Divyanshu Deoli, dmallia17, Dmitry Kobak, DS\_anas, Eduardo Jardim, EdwinWenink, EL-ATEIF Sara, Eleni Markou, EricEllwanger, Eric Fiegel, Erich Schubert, Ezri-Mudde, Fatos Morina, Felipe Rodrigues, Felix Hafner, Fenil Suchak, flyingdutchman23, Flynn, Fortune Uwha, Francois Berenger, Frankie Robertson, Frans Larsson, Frederick Robinson, frellwan, Gabriel S Vicente, Gael Varoquaux, genvalen, Geoffrey Thomas, geroldcsendes, Hleb Levitski, Glen, Glòria Macià Muñoz, gregorystrubel, groceryheist, Guillaume Lemaitre, guiweber, Haidar Almubarak, Hans Moritz Günther, Haoyin Xu, Harris Mirza, Harry Wei, Harutaka Kawamura, Hassan Alsawadi, Helder Geovane Gomes de Lima, Hugo DEFOIS, Igor Ilic, Ikko Ashimine, Isaack Mungui, Ishaan Bhat, Ishan Mishra, Iván Pulido, iwhalvic, J Alexander, Jack Liu, James Alan Preiss, James Budarz, James Lamb, Jannik, Jeff Zhao, Jennifer Maldonado, Jérémie du Boisberranger, Jesse Lima, Jianzhu Guo, jnboehm, Joel Nothman, JohanWork, John Paton, Jonathan Schneider, Jon Crall, Jon Haitz Legarreta Gorroño, Joris Van den Bossche, José Manuel Nápoles Duarte, Juan Carlos Alfaro Jiménez, Juan Martin Loyola, Julien Jerphanion, Julio Batista Silva, julyrashchenko, JVM, Kadatatlu Kishore, Karen Palacio, Kei Ishikawa, kmatt10, kobaski, Kot271828, Kunj, KurumeYuta, kxytim, lacrosse91, LalliAcqua, Laveen Bagai, Leonardo Rocco, Leonardo Uieda, Leopoldo Corona, Loic Esteve, LSturtew, Luca Bittarello, Luccas Quadros, Lucy Jiménez, Lucy Liu, ly648499246, Mabu Manaileng, Manimaran, makoeppel, Marco Gorelli, Maren Westermann, Mariangela, Maria Telenczuk, marielaraj, Martin Hirzel, Mateo Noreña, Mathieu Blondel, Mathis Batoul, mathurinm, Matthew Calcote, Maxime Prieur, Maxwell, Mehdi Hamoumi, Mehmet Ali Özer, Miao Cai, Michal Karbownik, michalkrawczyk, Mitzi, mlondschien, Mohamed Haseeb, Mohamed Khoualed, Muhammad Jarir Kanji, murata-yu, Nadim Kawwa, Nanshan Li, naozin555, Nate Parsons, Neal Fultz, Nic Annau, Nicolas Hug, Nicolas Miller, Nico Stefani, Nigel Bosch, Nikita Titov, Nodar Okroshiashvili, Norbert Preining, novaya, Ogbonna Chibuike Stephen, OGordon100, Oliver Pfaffel, Olivier Grisel, Oras Phongpanangam, Pablo Duque, Pablo Ibieta-Jimenez, Patric Lacouth, Paulo S. Costa, Paweł Olszewski, Peter Dye, PierreAttard, Pierre-Yves Le Borgne, PranayAnchuri, Prince Canuma, putschblos, qdeffense, RamyaNP, ranjanikrishnan, Ray Bell, Rene Jean Corneille, Reshama Shaikh, ricardojnf, RichardScottOZ, Rodion Martynov, Rohan Paul, Roman Lutz, Roman Yurchak, Samuel Brice, Sandy Khosasi, Sean Benhur J, Sebastian Flores, Sebastian Pölsterl, Shao Yang Hong, shinehide, shinnar, shivamgargsya, Shooter23, Shuhei Kayawari, Shyam Desai, simonamaggio, Sina Tootoonian, solosilence, Steven Kolawole, Steve Stagg, Surya Prakash, swpease, Sylvain Marié, Takeshi Oura, Terence Honles, TFiFiE, Thomas A Caswell, Thomas J. Fan, Tim Gates, TimotheeMathieu, Timothy Wolodzko, Tim Vink, t-jakubek, t-kusanagi, tliu68, Tobias Uhmann, tom1092, Tomás Moreyra, Tomás Ronald Hughes, Tom Dupré la Tour, Tommaso Di Noto, Tomohiro Endo, TONY GEORGE, Toshihiro NAKAE, tsuga, Uttam kumar, vadim-ushtanit, Vangelis Gkiastas, Venkatachalam N, Vilém Zouhar, Vinicius Rios Fuck, Vlasovets, waijean, Whidou, xavier dupré, xiaoyuchai, Yasmeen Alsaedy, yoch, Yosuke KOBAYASHI, Yu Feng, YusukeNagasaka, yzhenman, Zero, ZeyuSun, ZhaoweiWang, Zito, Zito Relova	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.10006330162286758, -0.04087422043085098, -0.000908754940610379, -0.0962100625038147, -0.04568716511130333, 0.03625895455479622, -0.06058185547590256, 0.05594674125313759, -0.010564901866018772, 0.024474482983350754, -0.034606996923685074, 0.05524132028222084, 0.035106170922517776, -0.00...	0.18475
Yosuke KOBAYASHI, Yu Feng, YusukeNagasaka, yzhenman, Zero, ZeyuSun, ZhaoweiWang, Zito, Zito Relova	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.0.rst	main	scikit-learn	[ -0.05371295288205147, 0.042615752667188644, -0.02580406330525875, -0.01981307938694954, -0.043513841927051544, 0.036550164222717285, 0.051876455545425415, 0.026911575347185135, 0.0011281418846920133, 0.011434040032327175, 0.047048553824424744, -0.07962996512651443, -0.022497959434986115, 0...	0.089479
.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_0\_23: ============ Version 0.23 ============ For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_0\_23\_0.py`. .. include:: changelog\_legend.inc .. \_changes\_0\_23\_2: Version 0.23.2 ============== 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. - \|Fix\| ``inertia\_`` attribute of :class:`cluster.KMeans` and :class:`cluster.MiniBatchKMeans`. 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.) Changelog --------- :mod:`sklearn.cluster` ...................... - \|Fix\| Fixed a bug in :class:`cluster.KMeans` where rounding errors could prevent convergence to be declared when `tol=0`. :pr:`17959` by :user:`Jérémie du Boisberranger `. - \|Fix\| Fixed a bug in :class:`cluster.KMeans` and :class:`cluster.MiniBatchKMeans` where the reported inertia was incorrectly weighted by the sample weights. :pr:`17848` by :user:`Jérémie du Boisberranger `. - \|Fix\| Fixed a bug in :class:`cluster.MeanShift` with `bin\_seeding=True`. When the estimated bandwidth is 0, the behavior is equivalent to `bin\_seeding=False`. :pr:`17742` by :user:`Jeremie du Boisberranger `. - \|Fix\| Fixed a bug in :class:`cluster.AffinityPropagation`, that gives incorrect clusters when the array dtype is float32. :pr:`17995` by :user:`Thomaz Santana ` and :user:`Amanda Dsouza `. :mod:`sklearn.decomposition` ............................ - \|Fix\| Fixed a bug in :func:`decomposition.MiniBatchDictionaryLearning.partial\_fit` which should update the dictionary by iterating only once over a mini-batch. :pr:`17433` by :user:`Chiara Marmo `. - \|Fix\| Avoid overflows on Windows in :func:`decomposition.IncrementalPCA.partial\_fit` for large ``batch\_size`` and ``n\_samples`` values. :pr:`17985` by :user:`Alan Butler ` and :user:`Amanda Dsouza `. :mod:`sklearn.ensemble` ....................... - \|Fix\| Fixed bug in `ensemble.MultinomialDeviance` where the average of logloss was incorrectly calculated as sum of logloss. :pr:`17694` by :user:`Markus Rempfler ` and :user:`Tsutomu Kusanagi `. - \|Fix\| Fixes :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor` compatibility with estimators that do not define `n\_features\_in\_`. :pr:`17357` by `Thomas Fan`\_. :mod:`sklearn.feature\_extraction` ................................. - \|Fix\| Fixes bug in :class:`feature\_extraction.text.CountVectorizer` where sample order invariance was broken when `max\_features` was set and features had the same count. :pr:`18016` by `Thomas Fan`\_, `Roman Yurchak`\_, and `Joel Nothman`\_. :mod:`sklearn.linear\_model` ........................... - \|Fix\| :func:`linear\_model.lars\_path` does not overwrite `X` when `X\_copy=True` and `Gram='auto'`. :pr:`17914` by `Thomas Fan`\_. :mod:`sklearn.manifold` ....................... - \|Fix\| Fixed a bug where :func:`metrics.pairwise\_distances` would raise an error if ``metric='seuclidean'`` and ``X`` is not type ``np.float64``. :pr:`15730` by :user:`Forrest Koch `. :mod:`sklearn.metrics` ...................... - \|Fix\| Fixed a bug in :func:`metrics.mean\_squared\_error` where the average of multiple RMSE values was incorrectly calculated as the root of the average of multiple MSE values. :pr:`17309` by :user:`Swier Heeres `. :mod:`sklearn.pipeline` ....................... - \|Fix\| :class:`pipeline.FeatureUnion` raises a deprecation warning when `None` is included in `transformer\_list`. :pr:`17360` by `Thomas Fan`\_. :mod:`sklearn.utils` .................... - \|Fix\| Fix :func:`utils.estimator\_checks.check\_estimator` so that all test cases support the `binary\_only` estimator tag. :pr:`17812` by :user:`Bruno Charron `. .. \_changes\_0\_23\_1: Version 0.23.1 ============== \\May 18 2020\\ Changelog --------- :mod:`sklearn.cluster` ...................... - \|Efficiency\| :class:`cluster.KMeans` efficiency has been improved for very small datasets. In particular it cannot spawn idle threads any more. :pr:`17210` and :pr:`17235` by :user:`Jeremie du Boisberranger `. - \|Fix\| Fixed a bug in :class:`cluster.KMeans` where the sample weights provided by the user were modified in place. :pr:`17204` by :user:`Jeremie du Boisberranger `. Miscellaneous ............. - \|Fix\| Fixed a bug in the `repr` of third-party estimators that use a `\\kwargs` parameter in their constructor, when `changed\_only` is True which is now the default. :pr:`17205` by `Nicolas Hug`\_. .. \_changes\_0\_23: Version 0.23.0 ============== \\May 12 2020\\ Enforcing keyword-only arguments -------------------------------- In an effort to promote clear and non-ambiguous use of the library, most constructor and function parameters are now expected to be passed	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.23.rst	main	scikit-learn	[ -0.033726830035448074, -0.06378750503063202, 0.07614924758672714, -0.01612834818661213, 0.13424484431743622, -0.01658194698393345, -0.009358977898955345, 0.00574225140735507, 0.018177580088377, -0.009480919688940048, 0.10667389631271362, -0.009158192202448845, -0.008170434273779392, -0.077...	0.023547
their constructor, when `changed\_only` is True which is now the default. :pr:`17205` by `Nicolas Hug`\_. .. \_changes\_0\_23: Version 0.23.0 ============== \\May 12 2020\\ Enforcing keyword-only arguments -------------------------------- In an effort to promote clear and non-ambiguous use of the library, most constructor and function parameters are now expected to be passed as keyword arguments (i.e. using the `param=value` syntax) instead of positional. To ease the transition, a `FutureWarning` is raised if a keyword-only parameter is used as positional. In version 1.0 (renaming of 0.25), these parameters will be strictly keyword-only, and a `TypeError` will be raised. :issue:`15005` by `Joel Nothman`\_, `Adrin Jalali`\_, `Thomas Fan`\_, and `Nicolas Hug`\_. See `SLEP009 `\_ for more details. 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. - \|Fix\| :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor`, and :class:`ensemble.IsolationForest`. - \|Fix\| :class:`cluster.KMeans` with ``algorithm="elkan"`` and ``algorithm="full"``. - \|Fix\| :class:`cluster.Birch` - \|Fix\| `compose.ColumnTransformer.get\_feature\_names` - \|Fix\| :func:`compose.ColumnTransformer.fit` - \|Fix\| :func:`datasets.make\_multilabel\_classification` - \|Fix\| :class:`decomposition.PCA` with `n\_components='mle'` - \|Enhancement\| :class:`decomposition.NMF` and :func:`decomposition.non\_negative\_factorization` with float32 dtype input. - \|Fix\| :func:`decomposition.KernelPCA.inverse\_transform` - \|API\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` - \|Fix\| ``estimator\_samples\_`` in :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` and :class:`ensemble.IsolationForest` - \|Fix\| :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor` with `sample\_weight` - \|Fix\| :class:`gaussian\_process.GaussianProcessRegressor` - \|Fix\| :class:`linear\_model.RANSACRegressor` with ``sample\_weight``. - \|Fix\| :class:`linear\_model.RidgeClassifierCV` - \|Fix\| :func:`metrics.mean\_squared\_error` with `squared` and `multioutput='raw\_values'`. - \|Fix\| :func:`metrics.mutual\_info\_score` with negative scores. - \|Fix\| :func:`metrics.confusion\_matrix` with zero length `y\_true` and `y\_pred` - \|Fix\| :class:`neural\_network.MLPClassifier` - \|Fix\| :class:`preprocessing.StandardScaler` with `partial\_fit` and sparse input. - \|Fix\| :class:`preprocessing.Normalizer` with norm='max' - \|Fix\| Any model using the `svm.libsvm` or the `svm.liblinear` solver, including :class:`svm.LinearSVC`, :class:`svm.LinearSVR`, :class:`svm.NuSVC`, :class:`svm.NuSVR`, :class:`svm.OneClassSVM`, :class:`svm.SVC`, :class:`svm.SVR`, :class:`linear\_model.LogisticRegression`. - \|Fix\| :class:`tree.DecisionTreeClassifier`, :class:`tree.ExtraTreeClassifier` and :class:`ensemble.GradientBoostingClassifier` as well as ``predict`` method of :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeRegressor`, and :class:`ensemble.GradientBoostingRegressor` and read-only float32 input in ``predict``, ``decision\_path`` and ``predict\_proba``. 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.) Changelog --------- .. 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.cluster` ...................... - \|Efficiency\| :class:`cluster.Birch` implementation of the predict method avoids high memory footprint by calculating the distances matrix using a chunked scheme. :pr:`16149` by :user:`Jeremie du Boisberranger ` and :user:`Alex Shacked `. - \|Efficiency\| \|MajorFeature\| The critical parts of :class:`cluster.KMeans` have a more optimized implementation. Parallelism is now over the data instead of over initializations allowing better scalability. :pr:`11950` by :user:`Jeremie du Boisberranger `. - \|Enhancement\| :class:`cluster.KMeans` now supports sparse data when `solver = "elkan"`. :pr:`11950` by :user:`Jeremie du Boisberranger `. - \|Enhancement\| :class:`cluster.AgglomerativeClustering` has a faster and more memory efficient implementation of single linkage clustering. :pr:`11514` by :user:`Leland McInnes `. - \|Fix\| :class:`cluster.KMeans` with ``algorithm="elkan"`` now converges with ``tol=0`` as with the default ``algorithm="full"``. :pr:`16075` by :user:`Erich Schubert `. - \|Fix\| Fixed a bug in :class:`cluster.Birch` where the `n\_clusters` parameter could not have a `np.int64` type. :pr:`16484` by :user:`Jeremie du Boisberranger `. - \|Fix\| :class:`cluster.AgglomerativeClustering` add specific error when distance matrix is not square and `affinity=precomputed`. :pr:`16257` by :user:`Simona Maggio `. - \|API\| The ``n\_jobs`` parameter of :class:`cluster.KMeans`, :class:`cluster.SpectralCoclustering` and :class:`cluster.SpectralBiclustering` is deprecated. They now use OpenMP based parallelism. For more details on how to	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.23.rst	main	scikit-learn	[ -0.11212655901908875, 0.034656524658203125, -0.005067559890449047, -0.06022948399186134, -0.005740574095398188, -0.04035300388932228, 0.0531909205019474, 0.009559414349496365, 0.002880098996683955, 0.030870378017425537, 0.10806197673082352, 0.014934942126274109, 0.028300151228904724, -0.03...	0.123672
type. :pr:`16484` by :user:`Jeremie du Boisberranger `. - \|Fix\| :class:`cluster.AgglomerativeClustering` add specific error when distance matrix is not square and `affinity=precomputed`. :pr:`16257` by :user:`Simona Maggio `. - \|API\| The ``n\_jobs`` parameter of :class:`cluster.KMeans`, :class:`cluster.SpectralCoclustering` and :class:`cluster.SpectralBiclustering` is deprecated. They now use OpenMP based parallelism. For more details on how to control the number of threads, please refer to our :ref:`parallelism` notes. :pr:`11950` by :user:`Jeremie du Boisberranger `. - \|API\| The ``precompute\_distances`` parameter of :class:`cluster.KMeans` is deprecated. It has no effect. :pr:`11950` by :user:`Jeremie du Boisberranger `. - \|API\| The ``random\_state`` parameter has been added to :class:`cluster.AffinityPropagation`. :pr:`16801` by :user:`rcwoolston` and :user:`Chiara Marmo `. :mod:`sklearn.compose` ...................... - \|Efficiency\| :class:`compose.ColumnTransformer` is now faster when working with dataframes and strings are used to specific subsets of data for transformers. :pr:`16431` by `Thomas Fan`\_. - \|Enhancement\| :class:`compose.ColumnTransformer` method ``get\_feature\_names`` now supports `'passthrough'` columns, with the feature name being either the column name for a dataframe, or `'xi'` for column index `i`. :pr:`14048` by :user:`Lewis Ball `. - \|Fix\| :class:`compose.ColumnTransformer` method ``get\_feature\_names`` now returns correct results when one of the transformer steps applies on an empty list of columns :pr:`15963` by `Roman Yurchak`\_. - \|Fix\| :func:`compose.ColumnTransformer.fit` will error when selecting a column name that is not unique in the dataframe. :pr:`16431` by `Thomas Fan`\_. :mod:`sklearn.datasets` ....................... - \|Efficiency\| :func:`datasets.fetch\_openml` has reduced memory usage because it no longer stores the full dataset text stream in memory. :pr:`16084` by `Joel Nothman`\_. - \|Feature\| :func:`datasets.fetch\_california\_housing` now supports heterogeneous data using pandas by setting `as\_frame=True`. :pr:`15950` by :user:`Stephanie Andrews ` and :user:`Reshama Shaikh `. - \|Feature\| embedded dataset loaders :func:`datasets.load\_breast\_cancer`, :func:`datasets.load\_diabetes`, :func:`datasets.load\_digits`, :func:`datasets.load\_iris`, :func:`datasets.load\_linnerud` and :func:`datasets.load\_wine` now support loading as a pandas ``DataFrame`` by setting `as\_frame=True`. :pr:`15980` by :user:`wconnell` and :user:`Reshama Shaikh `. - \|Enhancement\| Added ``return\_centers`` parameter in :func:`datasets.make\_blobs`, which can be used to return centers for each cluster. :pr:`15709` by :user:`shivamgargsya` and :user:`Venkatachalam N `. - \|Enhancement\| Functions :func:`datasets.make\_circles` and :func:`datasets.make\_moons` now accept two-element tuple. :pr:`15707` by :user:`Maciej J Mikulski `. - \|Fix\| :func:`datasets.make\_multilabel\_classification` now generates `ValueError` for arguments `n\_classes < 1` OR `length < 1`. :pr:`16006` by :user:`Rushabh Vasani `. - \|API\| The `StreamHandler` was removed from `sklearn.logger` to avoid double logging of messages in common cases where a handler is attached to the root logger, and to follow the Python logging documentation recommendation for libraries to leave the log message handling to users and application code. :pr:`16451` by :user:`Christoph Deil `. :mod:`sklearn.decomposition` ............................ - \|Enhancement\| :class:`decomposition.NMF` and :func:`decomposition.non\_negative\_factorization` now preserves float32 dtype. :pr:`16280` by :user:`Jeremie du Boisberranger `. - \|Enhancement\| :func:`decomposition.TruncatedSVD.transform` is now faster on given sparse ``csc`` matrices. :pr:`16837` by :user:`wornbb`. - \|Fix\| :class:`decomposition.PCA` with a float `n\_components` parameter, will exclusively choose the components that explain the variance greater than `n\_components`. :pr:`15669` by :user:`Krishna Chaitanya ` - \|Fix\| :class:`decomposition.PCA` with `n\_components='mle'` now correctly handles small eigenvalues, and does not infer 0 as the correct number of components. :pr:`16224` by :user:`Lisa Schwetlick `, and :user:`Gelavizh Ahmadi ` and :user:`Marija Vlajic Wheeler ` and :pr:`16841` by `Nicolas Hug`\_. - \|Fix\| :class:`decomposition.KernelPCA` method ``inverse\_transform`` now applies the correct inverse transform to the transformed data. :pr:`16655` by :user:`Lewis Ball `. - \|Fix\| Fixed bug that was causing :class:`decomposition.KernelPCA` to sometimes raise `invalid value encountered in multiply` during `fit`. :pr:`16718` by :user:`Gui Miotto `. - \|Feature\| Added `n\_components\_` attribute to :class:`decomposition.SparsePCA` and :class:`decomposition.MiniBatchSparsePCA`. :pr:`16981` by :user:`Mateusz Górski `. :mod:`sklearn.ensemble` ....................... - \|MajorFeature\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` now support :term:`sample\_weight`. :pr:`14696` by `Adrin Jalali`\_ and `Nicolas Hug`\_. - \|Feature\| Early stopping in :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` is now determined with a new `early\_stopping` parameter instead of `n\_iter\_no\_change`. Default value is 'auto', which enables early stopping if there are at least 10,000 samples	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.23.rst	main	scikit-learn	[ -0.034382473677396774, -0.039551060646772385, -0.08869927376508713, 0.0042626820504665375, -0.01830015517771244, -0.01055002212524414, -0.07138606905937195, 0.003938775043934584, 0.006753478664904833, 0.016127176582813263, 0.06752322614192963, -0.06794235110282898, -0.009973617270588875, -...	0.151605
....................... - \|MajorFeature\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` now support :term:`sample\_weight`. :pr:`14696` by `Adrin Jalali`\_ and `Nicolas Hug`\_. - \|Feature\| Early stopping in :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` is now determined with a new `early\_stopping` parameter instead of `n\_iter\_no\_change`. Default value is 'auto', which enables early stopping if there are at least 10,000 samples in the training set. :pr:`14516` by :user:`Johann Faouzi `. - \|MajorFeature\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` now support monotonic constraints, useful when features are supposed to have a positive/negative effect on the target. :pr:`15582` by `Nicolas Hug`\_. - \|API\| Added boolean `verbose` flag to classes: :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor`. :pr:`16069` by :user:`Sam Bail `, :user:`Hanna Bruce MacDonald `, :user:`Reshama Shaikh `, and :user:`Chiara Marmo `. - \|API\| Fixed a bug in :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` that would not respect the `max\_leaf\_nodes` parameter if the criteria was reached at the same time as the `max\_depth` criteria. :pr:`16183` by `Nicolas Hug`\_. - \|Fix\| Changed the convention for `max\_depth` parameter of :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`. The depth now corresponds to the number of edges to go from the root to the deepest leaf. Stumps (trees with one split) are now allowed. :pr:`16182` by :user:`Santhosh B ` - \|Fix\| Fixed a bug in :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` and :class:`ensemble.IsolationForest` where the attribute `estimators\_samples\_` did not generate the proper indices used during `fit`. :pr:`16437` by :user:`Jin-Hwan CHO `. - \|Fix\| Fixed a bug in :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor` where the `sample\_weight` argument was not being passed to `cross\_val\_predict` when evaluating the base estimators on cross-validation folds to obtain the input to the meta estimator. :pr:`16539` by :user:`Bill DeRose `. - \|Feature\| Added additional option `loss="poisson"` to :class:`ensemble.HistGradientBoostingRegressor`, which adds Poisson deviance with log-link useful for modeling count data. :pr:`16692` by :user:`Christian Lorentzen ` - \|Fix\| Fixed a bug where :class:`ensemble.HistGradientBoostingRegressor` and :class:`ensemble.HistGradientBoostingClassifier` would fail with multiple calls to fit when `warm\_start=True`, `early\_stopping=True`, and there is no validation set. :pr:`16663` by `Thomas Fan`\_. :mod:`sklearn.feature\_extraction` ................................. - \|Efficiency\| :class:`feature\_extraction.text.CountVectorizer` now sorts features after pruning them by document frequency. This improves performances for datasets with large vocabularies combined with ``min\_df`` or ``max\_df``. :pr:`15834` by :user:`Santiago M. Mola `. :mod:`sklearn.feature\_selection` ................................ - \|Enhancement\| Added support for multioutput data in :class:`feature\_selection.RFE` and :class:`feature\_selection.RFECV`. :pr:`16103` by :user:`Divyaprabha M `. - \|API\| Adds :class:`feature\_selection.SelectorMixin` back to public API. :pr:`16132` by :user:`trimeta`. :mod:`sklearn.gaussian\_process` ............................... - \|Enhancement\| :func:`gaussian\_process.kernels.Matern` returns the RBF kernel when ``nu=np.inf``. :pr:`15503` by :user:`Sam Dixon `. - \|Fix\| Fixed bug in :class:`gaussian\_process.GaussianProcessRegressor` that caused predicted standard deviations to only be between 0 and 1 when WhiteKernel is not used. :pr:`15782` by :user:`plgreenLIRU`. :mod:`sklearn.impute` ..................... - \|Enhancement\| :class:`impute.IterativeImputer` accepts both scalar and array-like inputs for ``max\_value`` and ``min\_value``. Array-like inputs allow a different max and min to be specified for each feature. :pr:`16403` by :user:`Narendra Mukherjee `. - \|Enhancement\| :class:`impute.SimpleImputer`, :class:`impute.KNNImputer`, and :class:`impute.IterativeImputer` accepts pandas' nullable integer dtype with missing values. :pr:`16508` by `Thomas Fan`\_. :mod:`sklearn.inspection` ......................... - \|Feature\| :func:`inspection.partial\_dependence` and `inspection.plot\_partial\_dependence` now support the fast 'recursion' method for :class:`ensemble.RandomForestRegressor` and :class:`tree.DecisionTreeRegressor`. :pr:`15864` by `Nicolas Hug`\_. :mod:`sklearn.linear\_model` ........................... - \|MajorFeature\| Added generalized linear models (GLM) with non normal error distributions, including :class:`linear\_model.PoissonRegressor`, :class:`linear\_model.GammaRegressor` and :class:`linear\_model.TweedieRegressor` which use Poisson, Gamma and Tweedie distributions respectively. :pr:`14300` by :user:`Christian Lorentzen `, `Roman Yurchak`\_, and `Olivier Grisel`\_. - \|MajorFeature\| Support of `sample\_weight` in :class:`linear\_model.ElasticNet` and :class:`linear\_model.Lasso` for dense feature matrix `X`. :pr:`15436` by :user:`Christian Lorentzen `. - \|Efficiency\| :class:`linear\_model.RidgeCV` and :class:`linear\_model.RidgeClassifierCV` now do not allocate a potentially large array to store dual coefficients for all hyperparameters during its `fit`, nor an array to store all error or LOO predictions unless `store\_cv\_values` is `True`. :pr:`15652` by :user:`Jérôme Dockès `. - \|Enhancement\| :class:`linear\_model.LassoLars` and :class:`linear\_model.Lars` now support a `jitter` parameter that adds random noise to the	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.23.rst	main	scikit-learn	[ -0.06298437714576721, -0.020894521847367287, 0.013393951579928398, 0.015505878254771233, 0.07346382737159729, 0.035094138234853745, -0.029772067442536354, 0.038613948971033096, -0.08676939457654953, 0.005964160896837711, -0.012054556049406528, -0.05088512599468231, -0.04532245919108391, -0...	0.075662
allocate a potentially large array to store dual coefficients for all hyperparameters during its `fit`, nor an array to store all error or LOO predictions unless `store\_cv\_values` is `True`. :pr:`15652` by :user:`Jérôme Dockès `. - \|Enhancement\| :class:`linear\_model.LassoLars` and :class:`linear\_model.Lars` now support a `jitter` parameter that adds random noise to the target. This might help with stability in some edge cases. :pr:`15179` by :user:`angelaambroz`. - \|Fix\| Fixed a bug where if a `sample\_weight` parameter was passed to the fit method of :class:`linear\_model.RANSACRegressor`, it would not be passed to the wrapped `base\_estimator` during the fitting of the final model. :pr:`15773` by :user:`Jeremy Alexandre `. - \|Fix\| Add `best\_score\_` attribute to :class:`linear\_model.RidgeCV` and :class:`linear\_model.RidgeClassifierCV`. :pr:`15655` by :user:`Jérôme Dockès `. - \|Fix\| Fixed a bug in :class:`linear\_model.RidgeClassifierCV` to pass a specific scoring strategy. Before the internal estimator outputs score instead of predictions. :pr:`14848` by :user:`Venkatachalam N `. - \|Fix\| :class:`linear\_model.LogisticRegression` will now avoid an unnecessary iteration when `solver='newton-cg'` by checking for inferior or equal instead of strictly inferior for maximum of `absgrad` and `tol` in `utils.optimize.\_newton\_cg`. :pr:`16266` by :user:`Rushabh Vasani `. - \|API\| Deprecated public attributes `standard\_coef\_`, `standard\_intercept\_`, `average\_coef\_`, and `average\_intercept\_` in :class:`linear\_model.SGDClassifier`, :class:`linear\_model.SGDRegressor`, :class:`linear\_model.PassiveAggressiveClassifier`, :class:`linear\_model.PassiveAggressiveRegressor`. :pr:`16261` by :user:`Carlos Brandt `. - \|Fix\| \|Efficiency\| :class:`linear\_model.ARDRegression` is more stable and much faster when `n\_samples > n\_features`. It can now scale to hundreds of thousands of samples. The stability fix might imply changes in the number of non-zero coefficients and in the predicted output. :pr:`16849` by `Nicolas Hug`\_. - \|Fix\| Fixed a bug in :class:`linear\_model.ElasticNetCV`, :class:`linear\_model.MultiTaskElasticNetCV`, :class:`linear\_model.LassoCV` and :class:`linear\_model.MultiTaskLassoCV` where fitting would fail when using joblib loky backend. :pr:`14264` by :user:`Jérémie du Boisberranger `. - \|Efficiency\| Speed up :class:`linear\_model.MultiTaskLasso`, :class:`linear\_model.MultiTaskLassoCV`, :class:`linear\_model.MultiTaskElasticNet`, :class:`linear\_model.MultiTaskElasticNetCV` by avoiding slower BLAS Level 2 calls on small arrays :pr:`17021` by :user:`Alex Gramfort ` and :user:`Mathurin Massias `. :mod:`sklearn.metrics` ...................... - \|Enhancement\| :func:`metrics.pairwise\_distances\_chunked` now allows its ``reduce\_func`` to not have a return value, enabling in-place operations. :pr:`16397` by `Joel Nothman`\_. - \|Fix\| Fixed a bug in :func:`metrics.mean\_squared\_error` to not ignore argument `squared` when argument `multioutput='raw\_values'`. :pr:`16323` by :user:`Rushabh Vasani ` - \|Fix\| Fixed a bug in :func:`metrics.mutual\_info\_score` where negative scores could be returned. :pr:`16362` by `Thomas Fan`\_. - \|Fix\| Fixed a bug in :func:`metrics.confusion\_matrix` that would raise an error when `y\_true` and `y\_pred` were length zero and `labels` was not `None`. In addition, we raise an error when an empty list is given to the `labels` parameter. :pr:`16442` by :user:`Kyle Parsons `. - \|API\| Changed the formatting of values in :meth:`metrics.ConfusionMatrixDisplay.plot` and `metrics.plot\_confusion\_matrix` to pick the shorter format (either '2g' or 'd'). :pr:`16159` by :user:`Rick Mackenbach ` and `Thomas Fan`\_. - \|API\| From version 0.25, :func:`metrics.pairwise\_distances` will no longer automatically compute the ``VI`` parameter for Mahalanobis distance and the ``V`` parameter for seuclidean distance if ``Y`` is passed. The user will be expected to compute this parameter on the training data of their choice and pass it to `pairwise\_distances`. :pr:`16993` by `Joel Nothman`\_. :mod:`sklearn.model\_selection` .............................. - \|Enhancement\| :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` yields stack trace information in fit failed warning messages in addition to previously emitted type and details. :pr:`15622` by :user:`Gregory Morse `. - \|Fix\| :func:`model\_selection.cross\_val\_predict` supports `method="predict\_proba"` when `y=None`. :pr:`15918` by :user:`Luca Kubin `. - \|Fix\| `model\_selection.fit\_grid\_point` is deprecated in 0.23 and will be removed in 0.25. :pr:`16401` by :user:`Arie Pratama Sutiono ` :mod:`sklearn.multioutput` .......................... - \|Feature\| :func:`multioutput.MultiOutputRegressor.fit` and :func:`multioutput.MultiOutputClassifier.fit` now can accept `fit\_params` to pass to the `estimator.fit` method of each step. :issue:`15953` :pr:`15959` by :user:`Ke Huang `. - \|Enhancement\| :class:`multioutput.RegressorChain` now supports `fit\_params` for `base\_estimator` during `fit`. :pr:`16111` by :user:`Venkatachalam N `. :mod:`sklearn.naive\_bayes` ............................. - \|Fix\| A correctly formatted error message is shown in :class:`naive\_bayes.CategoricalNB` when the number of features in	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.23.rst	main	scikit-learn	[ -0.0717618316411972, -0.007055338937789202, -0.09871102124452591, 0.023873573169112206, 0.023218102753162384, -0.010695358738303185, 0.01745709963142872, 0.007843522354960442, -0.13924314081668854, 0.021842649206519127, -0.006669057533144951, 0.0138109615072608, 0.006710545625537634, -0.07...	-0.030466
`fit\_params` to pass to the `estimator.fit` method of each step. :issue:`15953` :pr:`15959` by :user:`Ke Huang `. - \|Enhancement\| :class:`multioutput.RegressorChain` now supports `fit\_params` for `base\_estimator` during `fit`. :pr:`16111` by :user:`Venkatachalam N `. :mod:`sklearn.naive\_bayes` ............................. - \|Fix\| A correctly formatted error message is shown in :class:`naive\_bayes.CategoricalNB` when the number of features in the input differs between `predict` and `fit`. :pr:`16090` by :user:`Madhura Jayaratne `. :mod:`sklearn.neural\_network` ............................. - \|Efficiency\| :class:`neural\_network.MLPClassifier` and :class:`neural\_network.MLPRegressor` has reduced memory footprint when using stochastic solvers, `'sgd'` or `'adam'`, and `shuffle=True`. :pr:`14075` by :user:`meyer89`. - \|Fix\| Increases the numerical stability of the logistic loss function in :class:`neural\_network.MLPClassifier` by clipping the probabilities. :pr:`16117` by `Thomas Fan`\_. :mod:`sklearn.inspection` ......................... - \|Enhancement\| :class:`inspection.PartialDependenceDisplay` now exposes the deciles lines as attributes so they can be hidden or customized. :pr:`15785` by `Nicolas Hug`\_ :mod:`sklearn.preprocessing` ............................ - \|Feature\| argument `drop` of :class:`preprocessing.OneHotEncoder` will now accept value 'if\_binary' and will drop the first category of each feature with two categories. :pr:`16245` by :user:`Rushabh Vasani `. - \|Enhancement\| :class:`preprocessing.OneHotEncoder`'s `drop\_idx\_` ndarray can now contain `None`, where `drop\_idx\_[i] = None` means that no category is dropped for index `i`. :pr:`16585` by :user:`Chiara Marmo `. - \|Enhancement\| :class:`preprocessing.MaxAbsScaler`, :class:`preprocessing.MinMaxScaler`, :class:`preprocessing.StandardScaler`, :class:`preprocessing.PowerTransformer`, :class:`preprocessing.QuantileTransformer`, :class:`preprocessing.RobustScaler` now supports pandas' nullable integer dtype with missing values. :pr:`16508` by `Thomas Fan`\_. - \|Efficiency\| :class:`preprocessing.OneHotEncoder` is now faster at transforming. :pr:`15762` by `Thomas Fan`\_. - \|Fix\| Fix a bug in :class:`preprocessing.StandardScaler` which was incorrectly computing statistics when calling `partial\_fit` on sparse inputs. :pr:`16466` by :user:`Guillaume Lemaitre `. - \|Fix\| Fix a bug in :class:`preprocessing.Normalizer` with norm='max', which was not taking the absolute value of the maximum values before normalizing the vectors. :pr:`16632` by :user:`Maura Pintor ` and :user:`Battista Biggio `. :mod:`sklearn.semi\_supervised` .............................. - \|Fix\| :class:`semi\_supervised.LabelSpreading` and :class:`semi\_supervised.LabelPropagation` avoids divide by zero warnings when normalizing `label\_distributions\_`. :pr:`15946` by :user:`ngshya`. :mod:`sklearn.svm` .................. - \|Fix\| \|Efficiency\| Improved ``libsvm`` and ``liblinear`` random number generators used to randomly select coordinates in the coordinate descent algorithms. Platform-dependent C ``rand()`` was used, which is only able to generate numbers up to ``32767`` on windows platform (see this `blog post `\_) and also has poor randomization power as suggested by `this presentation `\_. It was replaced with C++11 ``mt19937``, a Mersenne Twister that correctly generates 31bits/63bits random numbers on all platforms. In addition, the crude "modulo" postprocessor used to get a random number in a bounded interval was replaced by the tweaked Lemire method as suggested by `this blog post `\_. Any model using the `svm.libsvm` or the `svm.liblinear` solver, including :class:`svm.LinearSVC`, :class:`svm.LinearSVR`, :class:`svm.NuSVC`, :class:`svm.NuSVR`, :class:`svm.OneClassSVM`, :class:`svm.SVC`, :class:`svm.SVR`, :class:`linear\_model.LogisticRegression`, is affected. In particular users can expect a better convergence when the number of samples (LibSVM) or the number of features (LibLinear) is large. :pr:`13511` by :user:`Sylvain Marié `. - \|Fix\| Fix use of custom kernel not taking float entries such as string kernels in :class:`svm.SVC` and :class:`svm.SVR`. Note that custom kernels are now expected to validate their input where they previously received valid numeric arrays. :pr:`11296` by `Alexandre Gramfort`\_ and :user:`Georgi Peev `. - \|API\| :class:`svm.SVR` and :class:`svm.OneClassSVM` attributes, `probA\_` and `probB\_`, are now deprecated as they were not useful. :pr:`15558` by `Thomas Fan`\_. :mod:`sklearn.tree` ................... - \|Fix\| :func:`tree.plot\_tree` `rotate` parameter was unused and has been deprecated. :pr:`15806` by :user:`Chiara Marmo `. - \|Fix\| Fix support of read-only float32 array input in ``predict``, ``decision\_path`` and ``predict\_proba`` methods of :class:`tree.DecisionTreeClassifier`, :class:`tree.ExtraTreeClassifier` and :class:`ensemble.GradientBoostingClassifier` as well as ``predict`` method of :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeRegressor`, and :class:`ensemble.GradientBoostingRegressor`. :pr:`16331` by :user:`Alexandre Batisse `. :mod:`sklearn.utils` .................... - \|MajorFeature\| Estimators can now be displayed with a rich html representation. This can be enabled in Jupyter notebooks by setting `display='diagram'` in :func:`~sklearn.set\_config`. The raw html can be returned by using :func:`utils.estimator\_html\_repr`. :pr:`14180`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.23.rst	main	scikit-learn	[ -0.07086728513240814, -0.01298518292605877, -0.0434722974896431, -0.0023286736104637384, -0.0018246922409161925, 0.006164794322103262, 0.010638406500220299, 0.0441986620426178, -0.10133204609155655, -0.012898042798042297, 0.09127283841371536, -0.09583771973848343, 0.08785052597522736, -0.1...	0.04691
well as ``predict`` method of :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeRegressor`, and :class:`ensemble.GradientBoostingRegressor`. :pr:`16331` by :user:`Alexandre Batisse `. :mod:`sklearn.utils` .................... - \|MajorFeature\| Estimators can now be displayed with a rich html representation. This can be enabled in Jupyter notebooks by setting `display='diagram'` in :func:`~sklearn.set\_config`. The raw html can be returned by using :func:`utils.estimator\_html\_repr`. :pr:`14180` by `Thomas Fan`\_. - \|Enhancement\| improve error message in :func:`utils.validation.column\_or\_1d`. :pr:`15926` by :user:`Loïc Estève `. - \|Enhancement\| add warning in :func:`utils.check\_array` for pandas sparse DataFrame. :pr:`16021` by :user:`Rushabh Vasani `. - \|Enhancement\| :func:`utils.check\_array` now constructs a sparse matrix from a pandas DataFrame that contains only `SparseArray` columns. :pr:`16728` by `Thomas Fan`\_. - \|Enhancement\| :func:`utils.check\_array` supports pandas' nullable integer dtype with missing values when `force\_all\_finite` is set to `False` or `'allow-nan'` in which case the data is converted to floating point values where `pd.NA` values are replaced by `np.nan`. As a consequence, all :mod:`sklearn.preprocessing` transformers that accept numeric inputs with missing values represented as `np.nan` now also accepts being directly fed pandas dataframes with `pd.Int\* or `pd.Uint\` typed columns that use `pd.NA` as a missing value marker. :pr:`16508` by `Thomas Fan`\_. - \|API\| Passing classes to :func:`utils.estimator\_checks.check\_estimator` and :func:`utils.estimator\_checks.parametrize\_with\_checks` is now deprecated, and support for classes will be removed in 0.24. Pass instances instead. :pr:`17032` by `Nicolas Hug`\_. - \|API\| The private utility `\_safe\_tags` in `utils.estimator\_checks` was removed, hence all tags should be obtained through `estimator.\_get\_tags()`. Note that Mixins like `RegressorMixin` must come \before\* base classes in the MRO for `\_get\_tags()` to work properly. :pr:`16950` by `Nicolas Hug`\_. - \|FIX\| `utils.all\_estimators` now only returns public estimators. :pr:`15380` by `Thomas Fan`\_. Miscellaneous ............. - \|MajorFeature\| Adds a HTML representation of estimators to be shown in a jupyter notebook or lab. This visualization is activated by setting the `display` option in :func:`sklearn.set\_config`. :pr:`14180` by `Thomas Fan`\_. - \|Enhancement\| ``scikit-learn`` now works with ``mypy`` without errors. :pr:`16726` by `Roman Yurchak`\_. - \|API\| Most estimators now expose a `n\_features\_in\_` attribute. This attribute is equal to the number of features passed to the `fit` method. See `SLEP010 `\_ for details. :pr:`16112` by `Nicolas Hug`\_. - \|API\| Estimators now have a `requires\_y` tags which is False by default except for estimators that inherit from `~sklearn.base.RegressorMixin` or `~sklearn.base.ClassifierMixin`. This tag is used to ensure that a proper error message is raised when y was expected but None was passed. :pr:`16622` by `Nicolas Hug`\_. - \|API\| The default setting `print\_changed\_only` has been changed from False to True. This means that the `repr` of estimators is now more concise and only shows the parameters whose default value has been changed when printing an estimator. You can restore the previous behaviour by using `sklearn.set\_config(print\_changed\_only=False)`. Also, note that it is always possible to quickly inspect the parameters of any estimator using `est.get\_params(deep=False)`. :pr:`17061` by `Nicolas Hug`\_. .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.22, including: Abbie Popa, Adrin Jalali, Aleksandra Kocot, Alexandre Batisse, Alexandre Gramfort, Alex Henrie, Alex Itkes, Alex Liang, alexshacked, Alonso Silva Allende, Ana Casado, Andreas Mueller, Angela Ambroz, Ankit810, Arie Pratama Sutiono, Arunav Konwar, Baptiste Maingret, Benjamin Beier Liu, bernie gray, Bharathi Srinivasan, Bharat Raghunathan, Bibhash Chandra Mitra, Brian Wignall, brigi, Brigitta Sipőcz, Carlos H Brandt, CastaChick, castor, cgsavard, Chiara Marmo, Chris Gregory, Christian Kastner, Christian Lorentzen, Corrie Bartelheimer, Daniël van Gelder, Daphne, David Breuer, david-cortes, dbauer9, Divyaprabha M, Edward Qian, Ekaterina Borovikova, ELNS, Emily Taylor, Erich Schubert, Eric Leung, Evgeni Chasnovski, Fabiana, Facundo Ferrín, Fan, Franziska Boenisch, Gael Varoquaux, Gaurav Sharma, Geoffrey Bolmier, Georgi Peev, gholdman1, Gonthier Nicolas, Gregory Morse, Gregory R. Lee, Guillaume Lemaitre, Gui Miotto, Hailey Nguyen, Hanmin Qin, Hao Chun	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.23.rst	main	scikit-learn	[ 0.007860995829105377, -0.004209492821246386, 0.009500169195234776, 0.028915196657180786, 0.10419140011072159, 0.02568877674639225, -0.10550951957702637, 0.027917223051190376, -0.03105834871530533, 0.042525000870227814, -0.02512619085609913, -0.06210736930370331, 0.060675155371427536, -0.08...	0.031635
Breuer, david-cortes, dbauer9, Divyaprabha M, Edward Qian, Ekaterina Borovikova, ELNS, Emily Taylor, Erich Schubert, Eric Leung, Evgeni Chasnovski, Fabiana, Facundo Ferrín, Fan, Franziska Boenisch, Gael Varoquaux, Gaurav Sharma, Geoffrey Bolmier, Georgi Peev, gholdman1, Gonthier Nicolas, Gregory Morse, Gregory R. Lee, Guillaume Lemaitre, Gui Miotto, Hailey Nguyen, Hanmin Qin, Hao Chun Chang, HaoYin, Hélion du Mas des Bourboux, Himanshu Garg, Hirofumi Suzuki, huangk10, Hugo van Kemenade, Hye Sung Jung, indecisiveuser, inderjeet, J-A16, Jérémie du Boisberranger, Jin-Hwan CHO, JJmistry, Joel Nothman, Johann Faouzi, Jon Haitz Legarreta Gorroño, Juan Carlos Alfaro Jiménez, judithabk6, jumon, Kathryn Poole, Katrina Ni, Kesshi Jordan, Kevin Loftis, Kevin Markham, krishnachaitanya9, Lam Gia Thuan, Leland McInnes, Lisa Schwetlick, lkubin, Loic Esteve, lopusz, lrjball, lucgiffon, lucyleeow, Lucy Liu, Lukas Kemkes, Maciej J Mikulski, Madhura Jayaratne, Magda Zielinska, maikia, Mandy Gu, Manimaran, Manish Aradwad, Maren Westermann, Maria, Mariana Meireles, Marie Douriez, Marielle, Mateusz Górski, mathurinm, Matt Hall, Maura Pintor, mc4229, meyer89, m.fab, Michael Shoemaker, Michał Słapek, Mina Naghshhnejad, mo, Mohamed Maskani, Mojca Bertoncelj, narendramukherjee, ngshya, Nicholas Won, Nicolas Hug, nicolasservel, Niklas, @nkish, Noa Tamir, Oleksandr Pavlyk, olicairns, Oliver Urs Lenz, Olivier Grisel, parsons-kyle-89, Paula, Pete Green, Pierre Delanoue, pspachtholz, Pulkit Mehta, Qizhi Jiang, Quang Nguyen, rachelcjordan, raduspaimoc, Reshama Shaikh, Riccardo Folloni, Rick Mackenbach, Ritchie Ng, Roman Feldbauer, Roman Yurchak, Rory Hartong-Redden, Rüdiger Busche, Rushabh Vasani, Sambhav Kothari, Samesh Lakhotia, Samuel Duan, SanthoshBala18, Santiago M. Mola, Sarat Addepalli, scibol, Sebastian Kießling, SergioDSR, Sergul Aydore, Shiki-H, shivamgargsya, SHUBH CHATTERJEE, Siddharth Gupta, simonamaggio, smarie, Snowhite, stareh, Stephen Blystone, Stephen Marsh, Sunmi Yoon, SylvainLan, talgatomarov, tamirlan1, th0rwas, theoptips, Thomas J Fan, Thomas Li, Thomas Schmitt, Tim Nonner, Tim Vink, Tiphaine Viard, Tirth Patel, Titus Christian, Tom Dupré la Tour, trimeta, Vachan D A, Vandana Iyer, Venkatachalam N, waelbenamara, wconnell, wderose, wenliwyan, Windber, wornbb, Yu-Hang "Maxin" Tang	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.23.rst	main	scikit-learn	[ -0.06202232837677002, -0.03307512402534485, -0.018860241398215294, 0.025282010436058044, -0.03361665830016136, 0.0743415579199791, -0.03749632090330124, 0.06714531779289246, -0.04177633300423622, -0.003581009339541197, 0.005485196132212877, -0.05267919972538948, -0.03964082524180412, -0.04...	0.065123
.. This file maps contributor names to their URLs. It should mostly be used for core contributors, and occasionally for contributors who do not want their github page to be their URL target. Historically it was used to hyperlink all contributors' names, and ``:user:`` should now be preferred. It also defines other ReST substitutions. .. role:: raw-html(raw) :format: html .. role:: raw-latex(raw) :format: latex .. \|MajorFeature\| replace:: :raw-html:`Major Feature` :raw-latex:`{\small\sc [Major Feature]}` .. \|Feature\| replace:: :raw-html:`Feature` :raw-latex:`{\small\sc [Feature]}` .. \|Efficiency\| replace:: :raw-html:`Efficiency` :raw-latex:`{\small\sc [Efficiency]}` .. \|Enhancement\| replace:: :raw-html:`Enhancement` :raw-latex:`{\small\sc [Enhancement]}` .. \|Fix\| replace:: :raw-html:`Fix` :raw-latex:`{\small\sc [Fix]}` .. \|API\| replace:: :raw-html:`API Change` :raw-latex:`{\small\sc [API Change]}` .. \_Olivier Grisel: https://bsky.app/profile/ogrisel.bsky.social .. \_Gael Varoquaux: https://gael-varoquaux.info .. \_Alexandre Gramfort: https://alexandre.gramfort.net .. \_Fabian Pedregosa: https://fa.bianp.net .. \_Mathieu Blondel: http://www.mblondel.org .. \_James Bergstra: http://www-etud.iro.umontreal.ca/~bergstrj/ .. \_liblinear: https://www.csie.ntu.edu.tw/~cjlin/liblinear/ .. \_Yaroslav Halchenko: http://www.onerussian.com/ .. \_Vlad Niculae: https://vene.ro/ .. \_Edouard Duchesnay: https://duchesnay.github.io/ .. \_Peter Prettenhofer: https://sites.google.com/site/peterprettenhofer/ .. \_Alexandre Passos: https://atpassos.me .. \_Nicolas Pinto: https://twitter.com/npinto .. \_Bertrand Thirion: https://team.inria.fr/parietal/bertrand-thirions-page .. \_Andreas Müller: https://amueller.github.io/ .. \_Matthieu Perrot: http://brainvisa.info/biblio/lnao/en/Author/PERROT-M.html .. \_Jake Vanderplas: https://staff.washington.edu/jakevdp/ .. \_Gilles Louppe: https://www.montefiore.ulg.ac.be/~glouppe/ .. \_INRIA: https://www.inria.fr/ .. \_Parietal Team: http://parietal.saclay.inria.fr/ .. \_David Warde-Farley: http://www-etud.iro.umontreal.ca/~wardefar/ .. \_Brian Holt: http://personal.ee.surrey.ac.uk/Personal/B.Holt .. \_Satrajit Ghosh: https://www.mit.edu/~satra/ .. \_Robert Layton: https://twitter.com/robertlayton .. \_Scott White: https://twitter.com/scottblanc .. \_David Marek: https://davidmarek.cz/ .. \_Christian Osendorfer: https://osdf.github.io .. \_Arnaud Joly: http://www.ajoly.org .. \_Rob Zinkov: https://www.zinkov.com/ .. \_Joel Nothman: https://joelnothman.com/ .. \_Nicolas Trésegnie: https://github.com/NicolasTr .. \_Kemal Eren: http://www.kemaleren.com .. \_Yann Dauphin: https://ynd.github.io/ .. \_Yannick Schwartz: https://team.inria.fr/parietal/schwarty/ .. \_Kyle Kastner: https://kastnerkyle.github.io/ .. \_Daniel Nouri: https://danielnouri.org .. \_Manoj Kumar: https://manojbits.wordpress.com .. \_Luis Pedro Coelho: https://luispedro.org .. \_Fares Hedyati: https://www.eecs.berkeley.edu/~fareshed .. \_Antony Lee: https://www.ocf.berkeley.edu/~antonyl/ .. \_Martin Billinger: https://tnsre.embs.org/author/martinbillinger/ .. \_Matteo Visconti di Oleggio Castello: http://www.mvdoc.me .. \_Trevor Stephens: https://trevorstephens.com/ .. \_Jan Hendrik Metzen: https://jmetzen.github.io/ .. \_Will Dawson: http://www.dawsonresearch.com .. \_Andrew Tulloch: https://tullo.ch/ .. \_Hanna Wallach: https://dirichlet.net/ .. \_Yan Yi: http://seowyanyi.org .. \_Hervé Bredin: https://herve.niderb.fr/ .. \_Eric Martin: http://www.ericmart.in .. \_Nicolas Goix: https://ngoix.github.io/ .. \_Sebastian Raschka: https://sebastianraschka.com/ .. \_Brian McFee: https://bmcfee.github.io .. \_Valentin Stolbunov: http://www.vstolbunov.com .. \_Jaques Grobler: https://github.com/jaquesgrobler .. \_Lars Buitinck: https://github.com/larsmans .. \_Loic Esteve: https://github.com/lesteve .. \_Noel Dawe: https://github.com/ndawe .. \_Raghav RV: https://github.com/raghavrv .. \_Tom Dupre la Tour: https://github.com/TomDLT .. \_Nelle Varoquaux: https://github.com/nellev .. \_Bing Tian Dai: https://github.com/btdai .. \_Dylan Werner-Meier: https://github.com/unautre .. \_Alyssa Batula: https://github.com/abatula .. \_Srivatsan Ramesh: https://github.com/srivatsan-ramesh .. \_Ron Weiss: https://www.ee.columbia.edu/~ronw/ .. \_Kathleen Chen: https://github.com/kchen17 .. \_Vincent Pham: https://github.com/vincentpham1991 .. \_Denis Engemann: https://denis-engemann.de .. \_Anish Shah: https://github.com/AnishShah .. \_Neeraj Gangwar: http://neerajgangwar.in .. \_Arthur Mensch: https://amensch.fr .. \_Joris Van den Bossche: https://github.com/jorisvandenbossche .. \_Roman Yurchak: https://github.com/rth .. \_Hanmin Qin: https://github.com/qinhanmin2014 .. \_Adrin Jalali: https://github.com/adrinjalali .. \_Thomas Fan: https://github.com/thomasjpfan .. \_Nicolas Hug: https://github.com/NicolasHug .. \_Guillaume Lemaitre: https://github.com/glemaitre .. \_Tim Head: https://betatim.github.io/	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/_contributors.rst	main	scikit-learn	[ -0.017190421000123024, 0.030149392783641815, 0.03786724433302879, 0.002187034348025918, 0.061843279749155045, 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.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_0\_22: ============ Version 0.22 ============ For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_0\_22\_0.py`. .. include:: changelog\_legend.inc .. \_changes\_0\_22\_2: Version 0.22.2.post1 ==================== \\March 3 2020\\ The 0.22.2.post1 release includes a packaging fix for the source distribution but the content of the packages is otherwise identical to the content of the wheels with the 0.22.2 version (without the .post1 suffix). Both contain the following changes. Changelog --------- :mod:`sklearn.impute` ..................... - \|Efficiency\| Reduce :func:`impute.KNNImputer` asymptotic memory usage by chunking pairwise distance computation. :pr:`16397` by `Joel Nothman`\_. :mod:`sklearn.metrics` ...................... - \|Fix\| Fixed a bug in `metrics.plot\_roc\_curve` where the name of the estimator was passed in the :class:`metrics.RocCurveDisplay` instead of the parameter `name`. It results in a different plot when calling :meth:`metrics.RocCurveDisplay.plot` for the subsequent times. :pr:`16500` by :user:`Guillaume Lemaitre `. - \|Fix\| Fixed a bug in `metrics.plot\_precision\_recall\_curve` where the name of the estimator was passed in the :class:`metrics.PrecisionRecallDisplay` instead of the parameter `name`. It results in a different plot when calling :meth:`metrics.PrecisionRecallDisplay.plot` for the subsequent times. :pr:`16505` by :user:`Guillaume Lemaitre `. :mod:`sklearn.neighbors` ........................ - \|Fix\| Fix a bug which converted a list of arrays into a 2-D object array instead of a 1-D array containing NumPy arrays. This bug was affecting :meth:`neighbors.NearestNeighbors.radius\_neighbors`. :pr:`16076` by :user:`Guillaume Lemaitre ` and :user:`Alex Shacked `. .. \_changes\_0\_22\_1: Version 0.22.1 ============== \\January 2 2020\\ This is a bug-fix release to primarily resolve some packaging issues in version 0.22.0. It also includes minor documentation improvements and some bug fixes. Changelog --------- :mod:`sklearn.cluster` ...................... - \|Fix\| :class:`cluster.KMeans` with ``algorithm="elkan"`` now uses the same stopping criterion as with the default ``algorithm="full"``. :pr:`15930` by :user:`inder128`. :mod:`sklearn.inspection` ......................... - \|Fix\| :func:`inspection.permutation\_importance` will return the same `importances` when a `random\_state` is given for both `n\_jobs=1` or `n\_jobs>1` both with shared memory backends (thread-safety) and isolated memory, process-based backends. Also avoid casting the data as object dtype and avoid read-only error on large dataframes with `n\_jobs>1` as reported in :issue:`15810`. Follow-up of :pr:`15898` by :user:`Shivam Gargsya `. :pr:`15933` by :user:`Guillaume Lemaitre ` and `Olivier Grisel`\_. - \|Fix\| `inspection.plot\_partial\_dependence` and :meth:`inspection.PartialDependenceDisplay.plot` now consistently checks the number of axes passed in. :pr:`15760` by `Thomas Fan`\_. :mod:`sklearn.metrics` ...................... - \|Fix\| `metrics.plot\_confusion\_matrix` now raises error when `normalize` is invalid. Previously, it runs fine with no normalization. :pr:`15888` by `Hanmin Qin`\_. - \|Fix\| `metrics.plot\_confusion\_matrix` now colors the label color correctly to maximize contrast with its background. :pr:`15936` by `Thomas Fan`\_ and :user:`DizietAsahi`. - \|Fix\| :func:`metrics.classification\_report` does no longer ignore the value of the ``zero\_division`` keyword argument. :pr:`15879` by :user:`Bibhash Chandra Mitra `. - \|Fix\| Fixed a bug in `metrics.plot\_confusion\_matrix` to correctly pass the `values\_format` parameter to the :class:`metrics.ConfusionMatrixDisplay` plot() call. :pr:`15937` by :user:`Stephen Blystone `. :mod:`sklearn.model\_selection` .............................. - \|Fix\| :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` accept scalar values provided in `fit\_params`. Change in 0.22 was breaking backward compatibility. :pr:`15863` by :user:`Adrin Jalali ` and :user:`Guillaume Lemaitre `. :mod:`sklearn.naive\_bayes` .......................... - \|Fix\| Removed `abstractmethod` decorator for the method `\_check\_X` in `naive\_bayes.BaseNB` that could break downstream projects inheriting from this deprecated public base class. :pr:`15996` by :user:`Brigitta Sipőcz `. :mod:`sklearn.preprocessing` ............................ - \|Fix\| :class:`preprocessing.QuantileTransformer` now guarantees the `quantiles\_` attribute to be completely sorted in non-decreasing manner. :pr:`15751` by :user:`Tirth Patel `. :mod:`sklearn.semi\_supervised` .............................. - \|Fix\| :class:`semi\_supervised.LabelPropagation` and :class:`semi\_supervised.LabelSpreading` now allow callable kernel function to return sparse weight matrix. :pr:`15868` by :user:`Niklas Smedemark-Margulies `. :mod:`sklearn.utils` .................... - \|Fix\| :func:`utils.check\_array` now correctly converts pandas DataFrame with boolean columns to floats. :pr:`15797` by `Thomas Fan`\_. - \|Fix\| :func:`utils.validation.check\_is\_fitted` accepts back an explicit ``attributes`` argument to check for specific attributes as explicit markers of a fitted estimator. When no explicit ``attributes`` are provided, only the attributes	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.07740248739719391, 0.013396418653428555, 0.05510397255420685, -0.057098183780908585, 0.11768600344657898, 0.003451687516644597, 0.036812685430049896, 0.0073052700608968735, -0.017536453902721405, -0.019258594140410423, 0.1444941908121109, -0.003711810801178217, -0.014473207294940948, -0...	-0.004369
`. :mod:`sklearn.utils` .................... - \|Fix\| :func:`utils.check\_array` now correctly converts pandas DataFrame with boolean columns to floats. :pr:`15797` by `Thomas Fan`\_. - \|Fix\| :func:`utils.validation.check\_is\_fitted` accepts back an explicit ``attributes`` argument to check for specific attributes as explicit markers of a fitted estimator. When no explicit ``attributes`` are provided, only the attributes that end with an underscore and do not start with double underscore are used as "fitted" markers. The ``all\_or\_any`` argument is also no longer deprecated. This change is made to restore some backward compatibility with the behavior of this utility in version 0.21. :pr:`15947` by `Thomas Fan`\_. .. \_changes\_0\_22: Version 0.22.0 ============== \\December 3 2019\\ Website update -------------- `Our website `\_ was revamped and given a fresh new look. :pr:`14849` by `Thomas Fan`\_. Clear definition of the public API ---------------------------------- Scikit-learn has a public API, and a private API. We do our best not to break the public API, and to only introduce backward-compatible changes that do not require any user action. However, in cases where that's not possible, any change to the public API is subject to a deprecation cycle of two minor versions. The private API isn't publicly documented and isn't subject to any deprecation cycle, so users should not rely on its stability. A function or object is public if it is documented in the `API Reference `\_ and if it can be imported with an import path without leading underscores. For example ``sklearn.pipeline.make\_pipeline`` is public, while `sklearn.pipeline.\_name\_estimators` is private. ``sklearn.ensemble.\_gb.BaseEnsemble`` is private too because the whole `\_gb` module is private. Up to 0.22, some tools were de-facto public (no leading underscore), while they should have been private in the first place. In version 0.22, these tools have been made properly private, and the public API space has been cleaned. In addition, importing from most sub-modules is now deprecated: you should for example use ``from sklearn.cluster import Birch`` instead of ``from sklearn.cluster.birch import Birch`` (in practice, ``birch.py`` has been moved to ``\_birch.py``). .. note:: All the tools in the public API should be documented in the `API Reference `\_. If you find a public tool (without leading underscore) that isn't in the API reference, that means it should either be private or documented. Please let us know by opening an issue! This work was tracked in `issue 9250 `\_ and `issue 12927 `\_. Deprecations: using ``FutureWarning`` from now on ------------------------------------------------- When deprecating a feature, previous versions of scikit-learn used to raise a ``DeprecationWarning``. Since the ``DeprecationWarnings`` aren't shown by default by Python, scikit-learn needed to resort to a custom warning filter to always show the warnings. That filter would sometimes interfere with users custom warning filters. Starting from version 0.22, scikit-learn will show ``FutureWarnings`` for deprecations, `as recommended by the Python documentation `\_. ``FutureWarnings`` are always shown by default by Python, so the custom filter has been removed and scikit-learn no longer hinders with user filters. :pr:`15080` by `Nicolas Hug`\_. 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:`cluster.KMeans` when `n\_jobs=1`. \|Fix\| - :class:`decomposition.SparseCoder`, :class:`decomposition.DictionaryLearning`, and :class:`decomposition.MiniBatchDictionaryLearning` \|Fix\| - :class:`decomposition.SparseCoder` with `algorithm='lasso\_lars'` \|Fix\| - :class:`decomposition.SparsePCA` where `normalize\_components` has no effect due to deprecation. - :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` \|Fix\|, \|Feature\|, \|Enhancement\|. - :class:`impute.IterativeImputer` when `X` has features with no missing values. \|Feature\| - :class:`linear\_model.Ridge` when `X` is sparse. \|Fix\| - :class:`model\_selection.StratifiedKFold` and any use of `cv=int` with a classifier. \|Fix\| - :class:`cross\_decomposition.CCA` when using scipy >= 1.3 \|Fix\| Details are listed in	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.014688000082969666, -0.029573192819952965, -0.06178028881549835, -0.011564462445676327, 0.1547517329454422, -0.07148803770542145, 0.03125899285078049, -0.07388795167207718, -0.0858922079205513, 0.045142270624637604, 0.11355598270893097, -0.07371238619089127, -0.038846760988235474, -0.02...	-0.020656
to deprecation. - :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` \|Fix\|, \|Feature\|, \|Enhancement\|. - :class:`impute.IterativeImputer` when `X` has features with no missing values. \|Feature\| - :class:`linear\_model.Ridge` when `X` is sparse. \|Fix\| - :class:`model\_selection.StratifiedKFold` and any use of `cv=int` with a classifier. \|Fix\| - :class:`cross\_decomposition.CCA` when using scipy >= 1.3 \|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.) Changelog --------- .. 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\| From version 0.24 :meth:`base.BaseEstimator.get\_params` will raise an AttributeError rather than return None for parameters that are in the estimator's constructor but not stored as attributes on the instance. :pr:`14464` by `Joel Nothman`\_. :mod:`sklearn.calibration` .......................... - \|Fix\| Fixed a bug that made :class:`calibration.CalibratedClassifierCV` fail when given a `sample\_weight` parameter of type `list` (in the case where `sample\_weights` are not supported by the wrapped estimator). :pr:`13575` by :user:`William de Vazelhes `. :mod:`sklearn.cluster` ...................... - \|Feature\| :class:`cluster.SpectralClustering` now accepts precomputed sparse neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`\_ and :user:`Kumar Ashutosh `. - \|Enhancement\| :class:`cluster.SpectralClustering` now accepts a ``n\_components`` parameter. This parameter extends `SpectralClustering` class functionality to match :meth:`cluster.spectral\_clustering`. :pr:`13726` by :user:`Shuzhe Xiao `. - \|Fix\| Fixed a bug where :class:`cluster.KMeans` produced inconsistent results between `n\_jobs=1` and `n\_jobs>1` due to the handling of the random state. :pr:`9288` by :user:`Bryan Yang `. - \|Fix\| Fixed a bug where `elkan` algorithm in :class:`cluster.KMeans` was producing Segmentation Fault on large arrays due to integer index overflow. :pr:`15057` by :user:`Vladimir Korolev `. - \|Fix\| :class:`~cluster.MeanShift` now accepts a :term:`max\_iter` with a default value of 300 instead of always using the default 300. It also now exposes an ``n\_iter\_`` indicating the maximum number of iterations performed on each seed. :pr:`15120` by `Adrin Jalali`\_. - \|Fix\| :class:`cluster.AgglomerativeClustering` and :class:`cluster.FeatureAgglomeration` now raise an error if `affinity='cosine'` and `X` has samples that are all-zeros. :pr:`7943` by :user:`mthorrell`. :mod:`sklearn.compose` ...................... - \|Feature\| Adds :func:`compose.make\_column\_selector` which is used with :class:`compose.ColumnTransformer` to select DataFrame columns on the basis of name and dtype. :pr:`12303` by `Thomas Fan`\_. - \|Fix\| Fixed a bug in :class:`compose.ColumnTransformer` which failed to select the proper columns when using a boolean list, with NumPy older than 1.12. :pr:`14510` by `Guillaume Lemaitre`\_. - \|Fix\| Fixed a bug in :class:`compose.TransformedTargetRegressor` which did not pass `\\fit\_params` to the underlying regressor. :pr:`14890` by :user:`Miguel Cabrera `. - \|Fix\| The :class:`compose.ColumnTransformer` now requires the number of features to be consistent between `fit` and `transform`. A `FutureWarning` is raised now, and this will raise an error in 0.24. If the number of features isn't consistent and negative indexing is used, an error is raised. :pr:`14544` by `Adrin Jalali`\_. :mod:`sklearn.cross\_decomposition` .................................. - \|Feature\| :class:`cross\_decomposition.PLSCanonical` and :class:`cross\_decomposition.PLSRegression` have a new function ``inverse\_transform`` to transform data to the original space. :pr:`15304` by :user:`Jaime Ferrando Huertas `. - \|Enhancement\| :class:`decomposition.KernelPCA` now properly checks the eigenvalues found by the solver for numerical or conditioning issues. This ensures consistency of results across solvers (different choices for ``eigen\_solver``), including approximate solvers such as ``'randomized'`` and ``'lobpcg'`` (see :issue:`12068`). :pr:`12145` by :user:`Sylvain Marié ` - \|Fix\| Fixed a bug where :class:`cross\_decomposition.PLSCanonical` and :class:`cross\_decomposition.PLSRegression` were raising an error when fitted with a target matrix `Y` in which the first column was	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.08420036733150482, 0.014575582928955555, 0.019582543522119522, 0.05974161624908447, 0.05911320820450783, -0.03291232883930206, -0.025667740032076836, 0.055223096162080765, -0.11575432121753693, -0.009027533233165741, 0.016845017671585083, -0.018022730946540833, 0.06756436824798584, -0.0...	0.001075
consistency of results across solvers (different choices for ``eigen\_solver``), including approximate solvers such as ``'randomized'`` and ``'lobpcg'`` (see :issue:`12068`). :pr:`12145` by :user:`Sylvain Marié ` - \|Fix\| Fixed a bug where :class:`cross\_decomposition.PLSCanonical` and :class:`cross\_decomposition.PLSRegression` were raising an error when fitted with a target matrix `Y` in which the first column was constant. :issue:`13609` by :user:`Camila Williamson `. - \|Fix\| :class:`cross\_decomposition.CCA` now produces the same results with scipy 1.3 and previous scipy versions. :pr:`15661` by `Thomas Fan`\_. :mod:`sklearn.datasets` ....................... - \|Feature\| :func:`datasets.fetch\_openml` now supports heterogeneous data using pandas by setting `as\_frame=True`. :pr:`13902` by `Thomas Fan`\_. - \|Feature\| :func:`datasets.fetch\_openml` now includes the `target\_names` in the returned Bunch. :pr:`15160` by `Thomas Fan`\_. - \|Enhancement\| The parameter `return\_X\_y` was added to :func:`datasets.fetch\_20newsgroups` and :func:`datasets.fetch\_olivetti\_faces` . :pr:`14259` by :user:`Sourav Singh `. - \|Enhancement\| :func:`datasets.make\_classification` now accepts array-like `weights` parameter, i.e. list or numpy.array, instead of list only. :pr:`14764` by :user:`Cat Chenal `. - \|Enhancement\| The parameter `normalize` was added to :func:`datasets.fetch\_20newsgroups\_vectorized`. :pr:`14740` by :user:`Stéphan Tulkens ` - \|Fix\| Fixed a bug in :func:`datasets.fetch\_openml`, which failed to load an OpenML dataset that contains an ignored feature. :pr:`14623` by :user:`Sarra Habchi `. :mod:`sklearn.decomposition` ............................ - \|Efficiency\| :class:`decomposition.NMF` with `solver="mu"` fitted on sparse input matrices now uses batching to avoid briefly allocating an array with size (#non-zero elements, n\_components). :pr:`15257` by :user:`Mart Willocx `. - \|Enhancement\| :func:`decomposition.dict\_learning` and :func:`decomposition.dict\_learning\_online` now accept `method\_max\_iter` and pass it to :meth:`decomposition.sparse\_encode`. :issue:`12650` by `Adrin Jalali`\_. - \|Enhancement\| :class:`decomposition.SparseCoder`, :class:`decomposition.DictionaryLearning`, and :class:`decomposition.MiniBatchDictionaryLearning` now take a `transform\_max\_iter` parameter and pass it to either :func:`decomposition.dict\_learning` or :func:`decomposition.sparse\_encode`. :issue:`12650` by `Adrin Jalali`\_. - \|Enhancement\| :class:`decomposition.IncrementalPCA` now accepts sparse matrices as input, converting them to dense in batches thereby avoiding the need to store the entire dense matrix at once. :pr:`13960` by :user:`Scott Gigante `. - \|Fix\| :func:`decomposition.sparse\_encode` now passes the `max\_iter` to the underlying :class:`linear\_model.LassoLars` when `algorithm='lasso\_lars'`. :issue:`12650` by `Adrin Jalali`\_. :mod:`sklearn.dummy` .................... - \|Fix\| :class:`dummy.DummyClassifier` now handles checking the existence of the provided constant in multioutput cases. :pr:`14908` by :user:`Martina G. Vilas `. - \|API\| The default value of the `strategy` parameter in :class:`dummy.DummyClassifier` will change from `'stratified'` in version 0.22 to `'prior'` in 0.24. A FutureWarning is raised when the default value is used. :pr:`15382` by `Thomas Fan`\_. - \|API\| The ``outputs\_2d\_`` attribute is deprecated in :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor`. It is equivalent to ``n\_outputs > 1``. :pr:`14933` by `Nicolas Hug`\_ :mod:`sklearn.ensemble` ....................... - \|MajorFeature\| Added :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor` to stack predictors using a final classifier or regressor. :pr:`11047` by :user:`Guillaume Lemaitre ` and :user:`Caio Oliveira ` and :pr:`15138` by :user:`Jon Cusick `.. - \|MajorFeature\| Many improvements were made to :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`: - \|Feature\| Estimators now natively support dense data with missing values both for training and predicting. They also support infinite values. :pr:`13911` and :pr:`14406` by `Nicolas Hug`\_, `Adrin Jalali`\_ and `Olivier Grisel`\_. - \|Feature\| Estimators now have an additional `warm\_start` parameter that enables warm starting. :pr:`14012` by :user:`Johann Faouzi `. - \|Feature\| :func:`inspection.partial\_dependence` and `inspection.plot\_partial\_dependence` now support the fast 'recursion' method for both estimators. :pr:`13769` by `Nicolas Hug`\_. - \|Enhancement\| for :class:`ensemble.HistGradientBoostingClassifier` the training loss or score is now monitored on a class-wise stratified subsample to preserve the class balance of the original training set. :pr:`14194` by :user:`Johann Faouzi `. - \|Enhancement\| :class:`ensemble.HistGradientBoostingRegressor` now supports the 'least\_absolute\_deviation' loss. :pr:`13896` by `Nicolas Hug`\_. - \|Fix\| Estimators now bin the training and validation data separately to avoid any data leak. :pr:`13933` by `Nicolas Hug`\_. - \|Fix\| Fixed a bug where early stopping would break with string targets. :pr:`14710` by `Guillaume Lemaitre`\_. - \|Fix\| :class:`ensemble.HistGradientBoostingClassifier` now raises an error if ``categorical\_crossentropy`` loss is given for a binary classification problem. :pr:`14869` by `Adrin Jalali`\_. Note that pickles	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.1008523479104042, -0.05159899592399597, -0.0132285850122571, 0.01080930046737194, 0.04626592621207237, -0.1317008137702942, -0.06675361841917038, -0.002080485923215747, -0.08506317436695099, 0.02895350567996502, 0.087028868496418, -0.01451869122684002, 0.02162114530801773, -0.0500856973...	-0.029951
to avoid any data leak. :pr:`13933` by `Nicolas Hug`\_. - \|Fix\| Fixed a bug where early stopping would break with string targets. :pr:`14710` by `Guillaume Lemaitre`\_. - \|Fix\| :class:`ensemble.HistGradientBoostingClassifier` now raises an error if ``categorical\_crossentropy`` loss is given for a binary classification problem. :pr:`14869` by `Adrin Jalali`\_. Note that pickles from 0.21 will not work in 0.22. - \|Enhancement\| Addition of ``max\_samples`` argument allows limiting size of bootstrap samples to be less than size of dataset. Added to :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`. :pr:`14682` by :user:`Matt Hancock ` and :pr:`5963` by :user:`Pablo Duboue `. - \|Fix\| :func:`ensemble.VotingClassifier.predict\_proba` will no longer be present when `voting='hard'`. :pr:`14287` by `Thomas Fan`\_. - \|Fix\| The `named\_estimators\_` attribute in :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor` now correctly maps to dropped estimators. Previously, the `named\_estimators\_` mapping was incorrect whenever one of the estimators was dropped. :pr:`15375` by `Thomas Fan`\_. - \|Fix\| Run by default :func:`utils.estimator\_checks.check\_estimator` on both :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor`. It leads to solve issues regarding shape consistency during `predict` which was failing when the underlying estimators were not outputting consistent array dimensions. Note that it should be replaced by refactoring the common tests in the future. :pr:`14305` by `Guillaume Lemaitre`\_. - \|Fix\| :class:`ensemble.AdaBoostClassifier` computes probabilities based on the decision function as in the literature. Thus, `predict` and `predict\_proba` give consistent results. :pr:`14114` by `Guillaume Lemaitre`\_. - \|Fix\| Stacking and Voting estimators now ensure that their underlying estimators are either all classifiers or all regressors. :class:`ensemble.StackingClassifier`, :class:`ensemble.StackingRegressor`, and :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor` now raise consistent error messages. :pr:`15084` by `Guillaume Lemaitre`\_. - \|Fix\| :class:`ensemble.AdaBoostRegressor` where the loss should be normalized by the max of the samples with non-null weights only. :pr:`14294` by `Guillaume Lemaitre`\_. - \|API\| ``presort`` is now deprecated in :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor`, and the parameter has no effect. Users are recommended to use :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` instead. :pr:`14907` by `Adrin Jalali`\_. :mod:`sklearn.feature\_extraction` ................................. - \|Enhancement\| A warning will now be raised if a parameter choice means that another parameter will be unused on calling the fit() method for :class:`feature\_extraction.text.HashingVectorizer`, :class:`feature\_extraction.text.CountVectorizer` and :class:`feature\_extraction.text.TfidfVectorizer`. :pr:`14602` by :user:`Gaurav Chawla `. - \|Fix\| Functions created by ``build\_preprocessor`` and ``build\_analyzer`` of `feature\_extraction.text.VectorizerMixin` can now be pickled. :pr:`14430` by :user:`Dillon Niederhut `. - \|Fix\| `feature\_extraction.text.strip\_accents\_unicode` now correctly removes accents from strings that are in NFKD normalized form. :pr:`15100` by :user:`Daniel Grady `. - \|Fix\| Fixed a bug that caused :class:`feature\_extraction.DictVectorizer` to raise an `OverflowError` during the `transform` operation when producing a `scipy.sparse` matrix on large input data. :pr:`15463` by :user:`Norvan Sahiner `. - \|API\| Deprecated unused `copy` param for :meth:`feature\_extraction.text.TfidfVectorizer.transform` it will be removed in v0.24. :pr:`14520` by :user:`Guillem G. Subies `. :mod:`sklearn.feature\_selection` ................................ - \|Enhancement\| Updated the following :mod:`sklearn.feature\_selection` estimators to allow NaN/Inf values in ``transform`` and ``fit``: :class:`feature\_selection.RFE`, :class:`feature\_selection.RFECV`, :class:`feature\_selection.SelectFromModel`, and :class:`feature\_selection.VarianceThreshold`. Note that if the underlying estimator of the feature selector does not allow NaN/Inf then it will still error, but the feature selectors themselves no longer enforce this restriction unnecessarily. :issue:`11635` by :user:`Alec Peters `. - \|Fix\| Fixed a bug where :class:`feature\_selection.VarianceThreshold` with `threshold=0` did not remove constant features due to numerical instability, by using range rather than variance in this case. :pr:`13704` by :user:`Roddy MacSween `. :mod:`sklearn.gaussian\_process` ............................... - \|Feature\| Gaussian process models on structured data: :class:`gaussian\_process.GaussianProcessRegressor` and :class:`gaussian\_process.GaussianProcessClassifier` can now accept a list of generic objects (e.g. strings, trees, graphs, etc.) as the ``X`` argument to their training/prediction methods. A user-defined kernel should be provided for computing the kernel matrix among the generic objects, and should inherit from `gaussian\_process.kernels.GenericKernelMixin` to notify the GPR/GPC model that it handles non-vectorial samples. :pr:`15557` by :user:`Yu-Hang Tang `. - \|Efficiency\| :func:`gaussian\_process.GaussianProcessClassifier.log\_marginal\_likelihood` and :func:`gaussian\_process.GaussianProcessRegressor.log\_marginal\_likelihood` now accept a ``clone\_kernel=True`` keyword argument. When set to ``False``,	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.07046043872833252, -0.02354923076927662, -0.010772679932415485, 0.025649478659033775, 0.08539091050624847, -0.035840954631567, -0.02196386083960533, 0.04262222349643707, -0.09604708850383759, 0.012686642818152905, -0.04118531569838524, -0.12271957844495773, 0.02605767361819744, -0.14142...	0.000928
A user-defined kernel should be provided for computing the kernel matrix among the generic objects, and should inherit from `gaussian\_process.kernels.GenericKernelMixin` to notify the GPR/GPC model that it handles non-vectorial samples. :pr:`15557` by :user:`Yu-Hang Tang `. - \|Efficiency\| :func:`gaussian\_process.GaussianProcessClassifier.log\_marginal\_likelihood` and :func:`gaussian\_process.GaussianProcessRegressor.log\_marginal\_likelihood` now accept a ``clone\_kernel=True`` keyword argument. When set to ``False``, the kernel attribute is modified, but may result in a performance improvement. :pr:`14378` by :user:`Masashi Shibata `. - \|API\| From version 0.24 :meth:`gaussian\_process.kernels.Kernel.get\_params` will raise an ``AttributeError`` rather than return ``None`` for parameters that are in the estimator's constructor but not stored as attributes on the instance. :pr:`14464` by `Joel Nothman`\_. :mod:`sklearn.impute` ..................... - \|MajorFeature\| Added :class:`impute.KNNImputer`, to impute missing values using k-Nearest Neighbors. :issue:`12852` by :user:`Ashim Bhattarai ` and `Thomas Fan`\_ and :pr:`15010` by `Guillaume Lemaitre`\_. - \|Feature\| :class:`impute.IterativeImputer` has new `skip\_compute` flag that is False by default, which, when True, will skip computation on features that have no missing values during the fit phase. :issue:`13773` by :user:`Sergey Feldman `. - \|Efficiency\| :meth:`impute.MissingIndicator.fit\_transform` avoid repeated computation of the masked matrix. :pr:`14356` by :user:`Harsh Soni `. - \|Fix\| :class:`impute.IterativeImputer` now works when there is only one feature. By :user:`Sergey Feldman `. - \|Fix\| Fixed a bug in :class:`impute.IterativeImputer` where features were imputed in the reverse desired order with ``imputation\_order`` either ``"ascending"`` or ``"descending"``. :pr:`15393` by :user:`Venkatachalam N `. :mod:`sklearn.inspection` ......................... - \|MajorFeature\| :func:`inspection.permutation\_importance` has been added to measure the importance of each feature in an arbitrary trained model with respect to a given scoring function. :issue:`13146` by `Thomas Fan`\_. - \|Feature\| :func:`inspection.partial\_dependence` and `inspection.plot\_partial\_dependence` now support the fast 'recursion' method for :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`. :pr:`13769` by `Nicolas Hug`\_. - \|Enhancement\| `inspection.plot\_partial\_dependence` has been extended to now support the new visualization API described in the :ref:`User Guide `. :pr:`14646` by `Thomas Fan`\_. - \|Enhancement\| :func:`inspection.partial\_dependence` accepts pandas DataFrame and :class:`pipeline.Pipeline` containing :class:`compose.ColumnTransformer`. In addition `inspection.plot\_partial\_dependence` will use the column names by default when a dataframe is passed. :pr:`14028` and :pr:`15429` by `Guillaume Lemaitre`\_. :mod:`sklearn.kernel\_approximation` ................................... - \|Fix\| Fixed a bug where :class:`kernel\_approximation.Nystroem` raised a `KeyError` when using `kernel="precomputed"`. :pr:`14706` by :user:`Venkatachalam N `. :mod:`sklearn.linear\_model` ........................... - \|Efficiency\| The 'liblinear' logistic regression solver is now faster and requires less memory. :pr:`14108`, :pr:`14170`, :pr:`14296` by :user:`Alex Henrie `. - \|Enhancement\| :class:`linear\_model.BayesianRidge` now accepts hyperparameters ``alpha\_init`` and ``lambda\_init`` which can be used to set the initial value of the maximization procedure in :term:`fit`. :pr:`13618` by :user:`Yoshihiro Uchida `. - \|Fix\| :class:`linear\_model.Ridge` now correctly fits an intercept when `X` is sparse, `solver="auto"` and `fit\_intercept=True`, because the default solver in this configuration has changed to `sparse\_cg`, which can fit an intercept with sparse data. :pr:`13995` by :user:`Jérôme Dockès `. - \|Fix\| :class:`linear\_model.Ridge` with `solver='sag'` now accepts F-ordered and non-contiguous arrays and makes a conversion instead of failing. :pr:`14458` by `Guillaume Lemaitre`\_. - \|Fix\| :class:`linear\_model.LassoCV` no longer forces ``precompute=False`` when fitting the final model. :pr:`14591` by `Andreas Müller`\_. - \|Fix\| :class:`linear\_model.RidgeCV` and :class:`linear\_model.RidgeClassifierCV` now correctly scores when `cv=None`. :pr:`14864` by :user:`Venkatachalam N `. - \|Fix\| Fixed a bug in :class:`linear\_model.LogisticRegressionCV` where the ``scores\_``, ``n\_iter\_`` and ``coefs\_paths\_`` attribute would have a wrong ordering with ``penalty='elastic-net'``. :pr:`15044` by `Nicolas Hug`\_ - \|Fix\| :class:`linear\_model.MultiTaskLassoCV` and :class:`linear\_model.MultiTaskElasticNetCV` with X of dtype int and `fit\_intercept=True`. :pr:`15086` by :user:`Alex Gramfort `. - \|Fix\| The liblinear solver now supports ``sample\_weight``. :pr:`15038` by `Guillaume Lemaitre`\_. :mod:`sklearn.manifold` ....................... - \|Feature\| :class:`manifold.Isomap`, :class:`manifold.TSNE`, and :class:`manifold.SpectralEmbedding` now accept precomputed sparse neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`\_ and :user:`Kumar Ashutosh `. - \|Feature\| Exposed the ``n\_jobs`` parameter in :class:`manifold.TSNE` for multi-core calculation of the neighbors graph. This parameter has no impact when ``metric="precomputed"`` or (``metric="euclidean"`` and ``method="exact"``). :issue:`15082` by `Roman Yurchak`\_. - \|Efficiency\| Improved efficiency	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.08225155621767044, -0.12172793596982956, -0.09701058268547058, 0.008057251572608948, 0.04712524265050888, -0.13434456288814545, 0.027526715770363808, 0.05736883729696274, -0.03220239654183388, -0.052377376705408096, 0.02895047329366207, -0.0034650531597435474, 0.026602763682603836, -0.0...	0.107978
precomputed sparse neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`\_ and :user:`Kumar Ashutosh `. - \|Feature\| Exposed the ``n\_jobs`` parameter in :class:`manifold.TSNE` for multi-core calculation of the neighbors graph. This parameter has no impact when ``metric="precomputed"`` or (``metric="euclidean"`` and ``method="exact"``). :issue:`15082` by `Roman Yurchak`\_. - \|Efficiency\| Improved efficiency of :class:`manifold.TSNE` when ``method="barnes-hut"`` by computing the gradient in parallel. :pr:`13213` by :user:`Thomas Moreau ` - \|Fix\| Fixed a bug where :func:`manifold.spectral\_embedding` (and therefore :class:`manifold.SpectralEmbedding` and :class:`cluster.SpectralClustering`) computed wrong eigenvalues with ``eigen\_solver='amg'`` when ``n\_samples < 5 \* n\_components``. :pr:`14647` by `Andreas Müller`\_. - \|Fix\| Fixed a bug in :func:`manifold.spectral\_embedding` used in :class:`manifold.SpectralEmbedding` and :class:`cluster.SpectralClustering` where ``eigen\_solver="amg"`` would sometimes result in a LinAlgError. :issue:`13393` by :user:`Andrew Knyazev ` :pr:`13707` by :user:`Scott White ` - \|API\| Deprecate ``training\_data\_`` unused attribute in :class:`manifold.Isomap`. :issue:`10482` by `Tom Dupre la Tour`\_. :mod:`sklearn.metrics` ...................... - \|MajorFeature\| `metrics.plot\_roc\_curve` has been added to plot roc curves. This function introduces the visualization API described in the :ref:`User Guide `. :pr:`14357` by `Thomas Fan`\_. - \|Feature\| Added a new parameter ``zero\_division`` to multiple classification metrics: :func:`metrics.precision\_score`, :func:`metrics.recall\_score`, :func:`metrics.f1\_score`, :func:`metrics.fbeta\_score`, :func:`metrics.precision\_recall\_fscore\_support`, :func:`metrics.classification\_report`. This allows to set returned value for ill-defined metrics. :pr:`14900` by :user:`Marc Torrellas Socastro `. - \|Feature\| Added the :func:`metrics.pairwise.nan\_euclidean\_distances` metric, which calculates euclidean distances in the presence of missing values. :issue:`12852` by :user:`Ashim Bhattarai ` and `Thomas Fan`\_. - \|Feature\| New ranking metrics :func:`metrics.ndcg\_score` and :func:`metrics.dcg\_score` have been added to compute Discounted Cumulative Gain and Normalized Discounted Cumulative Gain. :pr:`9951` by :user:`Jérôme Dockès `. - \|Feature\| `metrics.plot\_precision\_recall\_curve` has been added to plot precision recall curves. :pr:`14936` by `Thomas Fan`\_. - \|Feature\| `metrics.plot\_confusion\_matrix` has been added to plot confusion matrices. :pr:`15083` by `Thomas Fan`\_. - \|Feature\| Added multiclass support to :func:`metrics.roc\_auc\_score` with corresponding scorers `'roc\_auc\_ovr'`, `'roc\_auc\_ovo'`, `'roc\_auc\_ovr\_weighted'`, and `'roc\_auc\_ovo\_weighted'`. :pr:`12789` and :pr:`15274` by :user:`Kathy Chen `, :user:`Mohamed Maskani `, and `Thomas Fan`\_. - \|Feature\| Add :class:`metrics.mean\_tweedie\_deviance` measuring the Tweedie deviance for a given ``power`` parameter. Also add mean Poisson deviance :class:`metrics.mean\_poisson\_deviance` and mean Gamma deviance :class:`metrics.mean\_gamma\_deviance` that are special cases of the Tweedie deviance for ``power=1`` and ``power=2`` respectively. :pr:`13938` by :user:`Christian Lorentzen ` and `Roman Yurchak`\_. - \|Efficiency\| Improved performance of :func:`metrics.pairwise.manhattan\_distances` in the case of sparse matrices. :pr:`15049` by `Paolo Toccaceli `. - \|Enhancement\| The parameter ``beta`` in :func:`metrics.fbeta\_score` is updated to accept the zero and `float('+inf')` value. :pr:`13231` by :user:`Dong-hee Na `. - \|Enhancement\| Added parameter ``squared`` in :func:`metrics.mean\_squared\_error` to return root mean squared error. :pr:`13467` by :user:`Urvang Patel `. - \|Enhancement\| Allow computing averaged metrics in the case of no true positives. :pr:`14595` by `Andreas Müller`\_. - \|Enhancement\| Multilabel metrics now supports list of lists as input. :pr:`14865` :user:`Srivatsan Ramesh `, :user:`Herilalaina Rakotoarison `, :user:`Léonard Binet `. - \|Enhancement\| :func:`metrics.median\_absolute\_error` now supports ``multioutput`` parameter. :pr:`14732` by :user:`Agamemnon Krasoulis `. - \|Enhancement\| 'roc\_auc\_ovr\_weighted' and 'roc\_auc\_ovo\_weighted' can now be used as the :term:`scoring` parameter of model-selection tools. :pr:`14417` by `Thomas Fan`\_. - \|Enhancement\| :func:`metrics.confusion\_matrix` accepts a parameters `normalize` allowing to normalize the confusion matrix by column, rows, or overall. :pr:`15625` by `Guillaume Lemaitre `. - \|Fix\| Raise a ValueError in :func:`metrics.silhouette\_score` when a precomputed distance matrix contains non-zero diagonal entries. :pr:`12258` by :user:`Stephen Tierney `. - \|API\| ``scoring="neg\_brier\_score"`` should be used instead of ``scoring="brier\_score\_loss"`` which is now deprecated. :pr:`14898` by :user:`Stefan Matcovici `. :mod:`sklearn.model\_selection` .............................. - \|Efficiency\| Improved performance of multimetric scoring in :func:`model\_selection.cross\_validate`, :class:`model\_selection.GridSearchCV`, and :class:`model\_selection.RandomizedSearchCV`. :pr:`14593` by `Thomas Fan`\_. - \|Enhancement\| :class:`model\_selection.learning\_curve` now accepts parameter ``return\_times`` which can be used to retrieve computation times in order to plot model scalability (see learning\_curve example). :pr:`13938` by :user:`Hadrien Reboul #### `. - \|Enhancement\| :class:`model\_selection.RandomizedSearchCV` now accepts lists of parameter distributions. :pr:`14549` by `Andreas Müller`\_. - \|Fix\| Reimplemented	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.06475982069969177, 0.00434979097917676, -0.030902523547410965, -0.023233838379383087, -0.018324928358197212, -0.04145662114024162, -0.09412625432014465, -0.0017308794194832444, -0.10727224498987198, 0.0024984432384371758, 0.035476699471473694, -0.06265903264284134, 0.039751242846250534, ...	-0.003895
:pr:`14593` by `Thomas Fan`\_. - \|Enhancement\| :class:`model\_selection.learning\_curve` now accepts parameter ``return\_times`` which can be used to retrieve computation times in order to plot model scalability (see learning\_curve example). :pr:`13938` by :user:`Hadrien Reboul #### `. - \|Enhancement\| :class:`model\_selection.RandomizedSearchCV` now accepts lists of parameter distributions. :pr:`14549` by `Andreas Müller`\_. - \|Fix\| Reimplemented :class:`model\_selection.StratifiedKFold` to fix an issue where one test set could be `n\_classes` larger than another. Test sets should now be near-equally sized. :pr:`14704` by `Joel Nothman`\_. - \|Fix\| The `cv\_results\_` attribute of :class:`model\_selection.GridSearchCV` and :class:`model\_selection.RandomizedSearchCV` now only contains unfitted estimators. This potentially saves a lot of memory since the state of the estimators isn't stored. :pr:`#15096` by `Andreas Müller`\_. - \|API\| :class:`model\_selection.KFold` and :class:`model\_selection.StratifiedKFold` now raise a warning if `random\_state` is set but `shuffle` is False. This will raise an error in 0.24. :mod:`sklearn.multioutput` .......................... - \|Fix\| :class:`multioutput.MultiOutputClassifier` now has attribute ``classes\_``. :pr:`14629` by :user:`Agamemnon Krasoulis `. - \|Fix\| :class:`multioutput.MultiOutputClassifier` now has `predict\_proba` as property and can be checked with `hasattr`. :issue:`15488` :pr:`15490` by :user:`Rebekah Kim ` :mod:`sklearn.naive\_bayes` ............................... - \|MajorFeature\| Added :class:`naive\_bayes.CategoricalNB` that implements the Categorical Naive Bayes classifier. :pr:`12569` by :user:`Tim Bicker ` and :user:`Florian Wilhelm `. :mod:`sklearn.neighbors` ........................ - \|MajorFeature\| Added :class:`neighbors.KNeighborsTransformer` and :class:`neighbors.RadiusNeighborsTransformer`, which transform input dataset into a sparse neighbors graph. They give finer control on nearest neighbors computations and enable easy pipeline caching for multiple use. :issue:`10482` by `Tom Dupre la Tour`\_. - \|Feature\| :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.RadiusNeighborsRegressor`, and :class:`neighbors.LocalOutlierFactor` now accept precomputed sparse neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`\_ and :user:`Kumar Ashutosh `. - \|Feature\| :class:`neighbors.RadiusNeighborsClassifier` now supports predicting probabilities by using `predict\_proba` and supports more outlier\_label options: 'most\_frequent', or different outlier\_labels for multi-outputs. :pr:`9597` by :user:`Wenbo Zhao `. - \|Efficiency\| Efficiency improvements for :func:`neighbors.RadiusNeighborsClassifier.predict`. :pr:`9597` by :user:`Wenbo Zhao `. - \|Fix\| :class:`neighbors.KNeighborsRegressor` now throws error when `metric='precomputed'` and fit on non-square data. :pr:`14336` by :user:`Gregory Dexter `. :mod:`sklearn.neural\_network` ............................. - \|Feature\| Add `max\_fun` parameter in `neural\_network.BaseMultilayerPerceptron`, :class:`neural\_network.MLPRegressor`, and :class:`neural\_network.MLPClassifier` to give control over maximum number of function evaluation to not meet ``tol`` improvement. :issue:`9274` by :user:`Daniel Perry `. :mod:`sklearn.pipeline` ....................... - \|Enhancement\| :class:`pipeline.Pipeline` now supports :term:`score\_samples` if the final estimator does. :pr:`13806` by :user:`Anaël Beaugnon `. - \|Fix\| The `fit` in :class:`~pipeline.FeatureUnion` now accepts `fit\_params` to pass to the underlying transformers. :pr:`15119` by `Adrin Jalali`\_. - \|API\| `None` as a transformer is now deprecated in :class:`pipeline.FeatureUnion`. Please use `'drop'` instead. :pr:`15053` by `Thomas Fan`\_. :mod:`sklearn.preprocessing` ............................ - \|Efficiency\| :class:`preprocessing.PolynomialFeatures` is now faster when the input data is dense. :pr:`13290` by :user:`Xavier Dupré `. - \|Enhancement\| Avoid unnecessary data copy when fitting preprocessors :class:`preprocessing.StandardScaler`, :class:`preprocessing.MinMaxScaler`, :class:`preprocessing.MaxAbsScaler`, :class:`preprocessing.RobustScaler` and :class:`preprocessing.QuantileTransformer` which results in a slight performance improvement. :pr:`13987` by `Roman Yurchak`\_. - \|Fix\| KernelCenterer now throws error when fit on non-square :class:`preprocessing.KernelCenterer` :pr:`14336` by :user:`Gregory Dexter `. :mod:`sklearn.model\_selection` .............................. - \|Fix\| :class:`model\_selection.GridSearchCV` and `model\_selection.RandomizedSearchCV` now supports the `\_pairwise` property, which prevents an error during cross-validation for estimators with pairwise inputs (such as :class:`neighbors.KNeighborsClassifier` when :term:`metric` is set to 'precomputed'). :pr:`13925` by :user:`Isaac S. Robson ` and :pr:`15524` by :user:`Xun Tang `. :mod:`sklearn.svm` .................. - \|Enhancement\| :class:`svm.SVC` and :class:`svm.NuSVC` now accept a ``break\_ties`` parameter. This parameter results in :term:`predict` breaking the ties according to the confidence values of :term:`decision\_function`, if ``decision\_function\_shape='ovr'``, and the number of target classes > 2. :pr:`12557` by `Adrin Jalali`\_. - \|Enhancement\| SVM estimators now throw a more specific error when `kernel='precomputed'` and fit on non-square data. :pr:`14336` by :user:`Gregory Dexter `. - \|Fix\| :class:`svm.SVC`, :class:`svm.SVR`, :class:`svm.NuSVR` and :class:`svm.OneClassSVM` when received values negative or zero for parameter ``sample\_weight`` in method fit(), generated an invalid model. This behavior occurred only in some border scenarios. Now in these cases, fit()	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.04973616078495979, -0.007862202823162079, -0.09200964123010635, 0.08083508163690567, 0.05033935233950615, -0.0333850122988224, -0.027239229530096054, 0.06449957937002182, -0.03473794087767601, 0.01494224090129137, 0.05472522974014282, -0.024525795131921768, -0.027470659464597702, -0.017...	-0.024882
specific error when `kernel='precomputed'` and fit on non-square data. :pr:`14336` by :user:`Gregory Dexter `. - \|Fix\| :class:`svm.SVC`, :class:`svm.SVR`, :class:`svm.NuSVR` and :class:`svm.OneClassSVM` when received values negative or zero for parameter ``sample\_weight`` in method fit(), generated an invalid model. This behavior occurred only in some border scenarios. Now in these cases, fit() will fail with an Exception. :pr:`14286` by :user:`Alex Shacked `. - \|Fix\| The `n\_support\_` attribute of :class:`svm.SVR` and :class:`svm.OneClassSVM` was previously non-initialized, and had size 2. It has now size 1 with the correct value. :pr:`15099` by `Nicolas Hug`\_. - \|Fix\| fixed a bug in `BaseLibSVM.\_sparse\_fit` where n\_SV=0 raised a ZeroDivisionError. :pr:`14894` by :user:`Danna Naser `. - \|Fix\| The liblinear solver now supports ``sample\_weight``. :pr:`15038` by `Guillaume Lemaitre`\_. :mod:`sklearn.tree` ................... - \|Feature\| Adds minimal cost complexity pruning, controlled by ``ccp\_alpha``, to :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier`, :class:`tree.ExtraTreeRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`, :class:`ensemble.GradientBoostingClassifier`, and :class:`ensemble.GradientBoostingRegressor`. :pr:`12887` by `Thomas Fan`\_. - \|API\| ``presort`` is now deprecated in :class:`tree.DecisionTreeClassifier` and :class:`tree.DecisionTreeRegressor`, and the parameter has no effect. :pr:`14907` by `Adrin Jalali`\_. - \|API\| The ``classes\_`` and ``n\_classes\_`` attributes of :class:`tree.DecisionTreeRegressor` are now deprecated. :pr:`15028` by :user:`Mei Guan `, `Nicolas Hug`\_, and `Adrin Jalali`\_. :mod:`sklearn.utils` .................... - \|Feature\| :func:`~utils.estimator\_checks.check\_estimator` can now generate checks by setting `generate\_only=True`. Previously, running :func:`~utils.estimator\_checks.check\_estimator` will stop when the first check fails. With `generate\_only=True`, all checks can run independently and report the ones that are failing. Read more in :ref:`rolling\_your\_own\_estimator`. :pr:`14381` by `Thomas Fan`\_. - \|Feature\| Added a pytest specific decorator, :func:`~utils.estimator\_checks.parametrize\_with\_checks`, to parametrize estimator checks for a list of estimators. :pr:`14381` by `Thomas Fan`\_. - \|Feature\| A new random variable, `utils.fixes.loguniform` implements a log-uniform random variable (e.g., for use in RandomizedSearchCV). For example, the outcomes ``1``, ``10`` and ``100`` are all equally likely for ``loguniform(1, 100)``. See :issue:`11232` by :user:`Scott Sievert ` and :user:`Nathaniel Saul `, and `SciPy PR 10815 `\_. - \|Enhancement\| `utils.safe\_indexing` (now deprecated) accepts an ``axis`` parameter to index array-like across rows and columns. The column indexing can be done on NumPy array, SciPy sparse matrix, and Pandas DataFrame. An additional refactoring was done. :pr:`14035` and :pr:`14475` by `Guillaume Lemaitre`\_. - \|Enhancement\| :func:`utils.extmath.safe\_sparse\_dot` works between 3D+ ndarray and sparse matrix. :pr:`14538` by :user:`Jérémie du Boisberranger `. - \|Fix\| :func:`utils.check\_array` is now raising an error instead of casting NaN to integer. :pr:`14872` by `Roman Yurchak`\_. - \|Fix\| :func:`utils.check\_array` will now correctly detect numeric dtypes in pandas dataframes, fixing a bug where ``float32`` was upcast to ``float64`` unnecessarily. :pr:`15094` by `Andreas Müller`\_. - \|API\| The following utils have been deprecated and are now private: - ``choose\_check\_classifiers\_labels`` - ``enforce\_estimator\_tags\_y`` - ``mocking.MockDataFrame`` - ``mocking.CheckingClassifier`` - ``optimize.newton\_cg`` - ``random.random\_choice\_csc`` - ``utils.choose\_check\_classifiers\_labels`` - ``utils.enforce\_estimator\_tags\_y`` - ``utils.optimize.newton\_cg`` - ``utils.random.random\_choice\_csc`` - ``utils.safe\_indexing`` - ``utils.mocking`` - ``utils.fast\_dict`` - ``utils.seq\_dataset`` - ``utils.weight\_vector`` - ``utils.fixes.parallel\_helper`` (removed) - All of ``utils.testing`` except for ``all\_estimators`` which is now in ``utils``. :mod:`sklearn.isotonic` .................................. - \|Fix\| Fixed a bug where :class:`isotonic.IsotonicRegression.fit` raised error when `X.dtype == 'float32'` and `X.dtype != y.dtype`. :pr:`14902` by :user:`Lucas `. Miscellaneous ............. - \|Fix\| Port `lobpcg` from SciPy which implement some bug fixes but only available in 1.3+. :pr:`13609` and :pr:`14971` by `Guillaume Lemaitre`\_. - \|API\| Scikit-learn now converts any input data structure implementing a duck array to a numpy array (using ``\_\_array\_\_``) to ensure consistent behavior instead of relying on ``\_\_array\_function\_\_`` (see `NEP 18 `\_). :pr:`14702` by `Andreas Müller`\_. - \|API\| Replace manual checks with ``check\_is\_fitted``. Errors thrown when using a non-fitted estimators are now more uniform. :pr:`13013` by :user:`Agamemnon Krasoulis `. Changes to estimator checks --------------------------- These changes mostly affect library developers. - Estimators are now expected to raise a ``NotFittedError`` if ``predict`` or ``transform`` is called before ``fit``; previously an ``AttributeError`` or ``ValueError`` was acceptable.	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.05778815969824791, -0.012532650493085384, -0.024527253583073616, 0.033925171941518784, 0.03830605372786522, -0.07104646414518356, 0.02373838797211647, 0.0682591125369072, -0.08181404322385788, -0.017006469890475273, 0.096839040517807, -0.02397668920457363, 0.03049611859023571, -0.066399...	0.014055
thrown when using a non-fitted estimators are now more uniform. :pr:`13013` by :user:`Agamemnon Krasoulis `. Changes to estimator checks --------------------------- These changes mostly affect library developers. - Estimators are now expected to raise a ``NotFittedError`` if ``predict`` or ``transform`` is called before ``fit``; previously an ``AttributeError`` or ``ValueError`` was acceptable. :pr:`13013` by by :user:`Agamemnon Krasoulis `. - Binary only classifiers are now supported in estimator checks. Such classifiers need to have the `binary\_only=True` estimator tag. :pr:`13875` by `Trevor Stephens`\_. - Estimators are expected to convert input data (``X``, ``y``, ``sample\_weights``) to :class:`numpy.ndarray` and never call ``\_\_array\_function\_\_`` on the original datatype that is passed (see `NEP 18 `\_). :pr:`14702` by `Andreas Müller`\_. - `requires\_positive\_X` estimator tag (for models that require X to be non-negative) is now used by :meth:`utils.estimator\_checks.check\_estimator` to make sure a proper error message is raised if X contains some negative entries. :pr:`14680` by :user:`Alex Gramfort `. - Added check that pairwise estimators raise error on non-square data :pr:`14336` by :user:`Gregory Dexter `. - Added two common multioutput estimator tests `utils.estimator\_checks.check\_classifier\_multioutput` and `utils.estimator\_checks.check\_regressor\_multioutput`. :pr:`13392` by :user:`Rok Mihevc `. - \|Fix\| Added ``check\_transformer\_data\_not\_an\_array`` to checks where missing - \|Fix\| The estimators tags resolution now follows the regular MRO. They used to be overridable only once. :pr:`14884` by `Andreas Müller`\_. .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.21, including: Aaron Alphonsus, Abbie Popa, Abdur-Rahmaan Janhangeer, abenbihi, Abhinav Sagar, Abhishek Jana, Abraham K. Lagat, Adam J. Stewart, Aditya Vyas, Adrin Jalali, Agamemnon Krasoulis, Alec Peters, Alessandro Surace, Alexandre de Siqueira, Alexandre Gramfort, alexgoryainov, Alex Henrie, Alex Itkes, alexshacked, Allen Akinkunle, Anaël Beaugnon, Anders Kaseorg, Andrea Maldonado, Andrea Navarrete, Andreas Mueller, Andreas Schuderer, Andrew Nystrom, Angela Ambroz, Anisha Keshavan, Ankit Jha, Antonio Gutierrez, Anuja Kelkar, Archana Alva, arnaudstiegler, arpanchowdhry, ashimb9, Ayomide Bamidele, Baran Buluttekin, barrycg, Bharat Raghunathan, Bill Mill, Biswadip Mandal, blackd0t, Brian G. Barkley, Brian Wignall, Bryan Yang, c56pony, camilaagw, cartman\_nabana, catajara, Cat Chenal, Cathy, cgsavard, Charles Vesteghem, Chiara Marmo, Chris Gregory, Christian Lorentzen, Christos Aridas, Dakota Grusak, Daniel Grady, Daniel Perry, Danna Naser, DatenBergwerk, David Dormagen, deeplook, Dillon Niederhut, Dong-hee Na, Dougal J. Sutherland, DrGFreeman, Dylan Cashman, edvardlindelof, Eric Larson, Eric Ndirangu, Eunseop Jeong, Fanny, federicopisanu, Felix Divo, flaviomorelli, FranciDona, Franco M. Luque, Frank Hoang, Frederic Haase, g0g0gadget, Gabriel Altay, Gabriel do Vale Rios, Gael Varoquaux, ganevgv, gdex1, getgaurav2, Gideon Sonoiya, Gordon Chen, gpapadok, Greg Mogavero, Grzegorz Szpak, Guillaume Lemaitre, Guillem García Subies, H4dr1en, hadshirt, Hailey Nguyen, Hanmin Qin, Hannah Bruce Macdonald, Harsh Mahajan, Harsh Soni, Honglu Zhang, Hossein Pourbozorg, Ian Sanders, Ingrid Spielman, J-A16, jaehong park, Jaime Ferrando Huertas, James Hill, James Myatt, Jay, jeremiedbb, Jérémie du Boisberranger, jeromedockes, Jesper Dramsch, Joan Massich, Joanna Zhang, Joel Nothman, Johann Faouzi, Jonathan Rahn, Jon Cusick, Jose Ortiz, Kanika Sabharwal, Katarina Slama, kellycarmody, Kennedy Kang'ethe, Kensuke Arai, Kesshi Jordan, Kevad, Kevin Loftis, Kevin Winata, Kevin Yu-Sheng Li, Kirill Dolmatov, Kirthi Shankar Sivamani, krishna katyal, Lakshmi Krishnan, Lakshya KD, LalliAcqua, lbfin, Leland McInnes, Léonard Binet, Loic Esteve, loopyme, lostcoaster, Louis Huynh, lrjball, Luca Ionescu, Lutz Roeder, MaggieChege, Maithreyi Venkatesh, Maltimore, Maocx, Marc Torrellas, Marie Douriez, Markus, Markus Frey, Martina G. Vilas, Martin Oywa, Martin Thoma, Masashi SHIBATA, Maxwell Aladago, mbillingr, m-clare, Meghann Agarwal, m.fab, Micah Smith, miguelbarao, Miguel Cabrera, Mina Naghshhnejad, Ming Li, motmoti, mschaffenroth, mthorrell, Natasha Borders, nezar-a, Nicolas Hug, Nidhin Pattaniyil, Nikita Titov, Nishan Singh Mann, Nitya Mandyam, norvan, notmatthancock, novaya, nxorable, Oleg Stikhin, Oleksandr Pavlyk, Olivier Grisel, Omar Saleem, Owen Flanagan, panpiort8, Paolo, Paolo Toccaceli, Paresh Mathur, Paula, Peng Yu, Peter Marko, pierretallotte, poorna-kumar, pspachtholz, qdeffense, Rajat Garg, Raphaël Bournhonesque, Ray, Ray Bell, Rebekah Kim, Reza	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.1072879508137703, -0.042358703911304474, -0.07465095818042755, -0.001227127155289054, 0.09666988253593445, -0.060777436941862106, 0.007181761786341667, -0.005174516700208187, -0.07958448678255081, 0.045140497386455536, 0.025604363530874252, -0.08677513897418976, 0.07070039957761765, -0....	0.104923
Nicolas Hug, Nidhin Pattaniyil, Nikita Titov, Nishan Singh Mann, Nitya Mandyam, norvan, notmatthancock, novaya, nxorable, Oleg Stikhin, Oleksandr Pavlyk, Olivier Grisel, Omar Saleem, Owen Flanagan, panpiort8, Paolo, Paolo Toccaceli, Paresh Mathur, Paula, Peng Yu, Peter Marko, pierretallotte, poorna-kumar, pspachtholz, qdeffense, Rajat Garg, Raphaël Bournhonesque, Ray, Ray Bell, Rebekah Kim, Reza Gharibi, Richard Payne, Richard W, rlms, Robert Juergens, Rok Mihevc, Roman Feldbauer, Roman Yurchak, R Sanjabi, RuchitaGarde, Ruth Waithera, Sackey, Sam Dixon, Samesh Lakhotia, Samuel Taylor, Sarra Habchi, Scott Gigante, Scott Sievert, Scott White, Sebastian Pölsterl, Sergey Feldman, SeWook Oh, she-dares, Shreya V, Shubham Mehta, Shuzhe Xiao, SimonCW, smarie, smujjiga, Sönke Behrends, Soumirai, Sourav Singh, stefan-matcovici, steinfurt, Stéphane Couvreur, Stephan Tulkens, Stephen Cowley, Stephen Tierney, SylvainLan, th0rwas, theoptips, theotheo, Thierno Ibrahima DIOP, Thomas Edwards, Thomas J Fan, Thomas Moreau, Thomas Schmitt, Tilen Kusterle, Tim Bicker, Timsaur, Tim Staley, Tirth Patel, Tola A, Tom Augspurger, Tom Dupré la Tour, topisan, Trevor Stephens, ttang131, Urvang Patel, Vathsala Achar, veerlosar, Venkatachalam N, Victor Luzgin, Vincent Jeanselme, Vincent Lostanlen, Vladimir Korolev, vnherdeiro, Wenbo Zhao, Wendy Hu, willdarnell, William de Vazelhes, wolframalpha, xavier dupré, xcjason, x-martian, xsat, xun-tang, Yinglr, yokasre, Yu-Hang "Maxin" Tang, Yulia Zamriy, Zhao Feng	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v0.22.rst	main	scikit-learn	[ -0.014402377419173717, -0.07332067936658859, -0.04842895269393921, 0.013826063834130764, -0.06114416942000389, 0.07337342202663422, -0.024784160777926445, 0.11731477826833725, -0.020265115424990654, -0.0015915759140625596, -0.013052292168140411, -0.02031005173921585, -0.014394338242709637, ...	0.107016
.. include:: \_contributors.rst .. currentmodule:: sklearn .. \_release\_notes\_1\_1: =========== Version 1.1 =========== For a short description of the main highlights of the release, please refer to :ref:`sphx\_glr\_auto\_examples\_release\_highlights\_plot\_release\_highlights\_1\_1\_0.py`. .. include:: changelog\_legend.inc .. \_changes\_1\_1\_3: Version 1.1.3 ============= \\October 2022\\ This bugfix release only includes fixes for compatibility with the latest SciPy release >= 1.9.2. Notable changes include: - \|Fix\| Include `msvcp140.dll` in the scikit-learn wheels since it has been removed in the latest SciPy wheels. :pr:`24631` by :user:`Chiara Marmo `. - \|Enhancement\| Create wheels for Python 3.11. :pr:`24446` by :user:`Chiara Marmo `. Other bug fixes will be available in the next 1.2 release, which will be released in the coming weeks. Note that support for 32-bit Python on Windows has been dropped in this release. This is due to the fact that SciPy 1.9.2 also dropped the support for that platform. Windows users are advised to install the 64-bit version of Python instead. .. \_changes\_1\_1\_2: Version 1.1.2 ============= \\August 2022\\ 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. - \|Fix\| :class:`manifold.TSNE` now throws a `ValueError` when fit with `perplexity>=n\_samples` to ensure mathematical correctness of the algorithm. :pr:`10805` by :user:`Mathias Andersen ` and :pr:`23471` by :user:`Meekail Zain `. Changelog --------- - \|Fix\| A default HTML representation is shown for meta-estimators with invalid parameters. :pr:`24015` by `Thomas Fan`\_. - \|Fix\| Add support for F-contiguous arrays for estimators and functions whose back-end have been changed in 1.1. :pr:`23990` by :user:`Julien Jerphanion `. - \|Fix\| Wheels are now available for MacOS 10.9 and greater. :pr:`23833` by `Thomas Fan`\_. :mod:`sklearn.base` ................... - \|Fix\| The `get\_params` method of the :class:`base.BaseEstimator` class now supports estimators with `type`-type params that have the `get\_params` method. :pr:`24017` by :user:`Henry Sorsky `. :mod:`sklearn.cluster` ...................... - \|Fix\| Fixed a bug in :class:`cluster.Birch` that could trigger an error when splitting a node if there are duplicates in the dataset. :pr:`23395` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.feature\_selection` ................................ - \|Fix\| :class:`feature\_selection.SelectFromModel` defaults to selection threshold 1e-5 when the estimator is either :class:`linear\_model.ElasticNet` or :class:`linear\_model.ElasticNetCV` with `l1\_ratio` equals 1 or :class:`linear\_model.LassoCV`. :pr:`23636` by :user:`Hao Chun Chang `. :mod:`sklearn.impute` ..................... - \|Fix\| :class:`impute.SimpleImputer` uses the dtype seen in `fit` for `transform` when the dtype is object. :pr:`22063` by `Thomas Fan`\_. :mod:`sklearn.linear\_model` ........................... - \|Fix\| Use dtype-aware tolerances for the validation of gram matrices (passed by users or precomputed). :pr:`22059` by :user:`Malte S. Kurz `. - \|Fix\| Fixed an error in :class:`linear\_model.LogisticRegression` with `solver="newton-cg"`, `fit\_intercept=True`, and a single feature. :pr:`23608` by `Tom Dupre la Tour`\_. :mod:`sklearn.manifold` ....................... - \|Fix\| :class:`manifold.TSNE` now throws a `ValueError` when fit with `perplexity>=n\_samples` to ensure mathematical correctness of the algorithm. :pr:`10805` by :user:`Mathias Andersen ` and :pr:`23471` by :user:`Meekail Zain `. :mod:`sklearn.metrics` ...................... - \|Fix\| Fixed error message of :class:`metrics.coverage\_error` for 1D array input. :pr:`23548` by :user:`Hao Chun Chang `. :mod:`sklearn.preprocessing` ............................ - \|Fix\| :meth:`preprocessing.OrdinalEncoder.inverse\_transform` correctly handles use cases where `unknown\_value` or `encoded\_missing\_value` is `nan`. :pr:`24087` by `Thomas Fan`\_. :mod:`sklearn.tree` ................... - \|Fix\| Fixed invalid memory access bug during fit in :class:`tree.DecisionTreeRegressor` and :class:`tree.DecisionTreeClassifier`. :pr:`23273` by `Thomas Fan`\_. .. \_changes\_1\_1\_1: Version 1.1.1 ============= \\May 2022\\ Changelog --------- - \|Enhancement\| The error message is improved when importing :class:`model\_selection.HalvingGridSearchCV`, :class:`model\_selection.HalvingRandomSearchCV`, or :class:`impute.IterativeImputer` without importing the experimental flag. :pr:`23194` by `Thomas Fan`\_. - \|Enhancement\| Added an extension in doc/conf.py to automatically generate the list of estimators that handle NaN values. :pr:`23198` by :user:`Lise Kleiber `, :user:`Zhehao Liu ` and :user:`Chiara Marmo `. :mod:`sklearn.datasets` ....................... - \|Fix\| Avoid timeouts in :func:`datasets.fetch\_openml` by not	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst	main	scikit-learn	[ -0.08715984970331192, -0.05246703326702118, 0.03696388006210327, -0.03028387948870659, 0.11771683394908905, 0.008436629548668861, -0.008353214710950851, 0.013335679657757282, -0.05581911280751228, -0.004707752261310816, 0.09586386382579803, 0.013892186805605888, -0.04163792356848717, 0.000...	0.025549
:class:`impute.IterativeImputer` without importing the experimental flag. :pr:`23194` by `Thomas Fan`\_. - \|Enhancement\| Added an extension in doc/conf.py to automatically generate the list of estimators that handle NaN values. :pr:`23198` by :user:`Lise Kleiber `, :user:`Zhehao Liu ` and :user:`Chiara Marmo `. :mod:`sklearn.datasets` ....................... - \|Fix\| Avoid timeouts in :func:`datasets.fetch\_openml` by not passing a `timeout` argument, :pr:`23358` by :user:`Loïc Estève `. :mod:`sklearn.decomposition` ............................ - \|Fix\| Avoid spurious warning in :class:`decomposition.IncrementalPCA` when `n\_samples == n\_components`. :pr:`23264` by :user:`Lucy Liu `. :mod:`sklearn.feature\_selection` ................................ - \|Fix\| The `partial\_fit` method of :class:`feature\_selection.SelectFromModel` now conducts validation for `max\_features` and `feature\_names\_in` parameters. :pr:`23299` by :user:`Long Bao `. :mod:`sklearn.metrics` ...................... - \|Fix\| Fixes :func:`metrics.precision\_recall\_curve` to compute precision-recall at 100% recall. The Precision-Recall curve now displays the last point corresponding to a classifier that always predicts the positive class: recall=100% and precision=class balance. :pr:`23214` by :user:`Stéphane Collot ` and :user:`Max Baak `. :mod:`sklearn.preprocessing` ............................ - \|Fix\| :class:`preprocessing.PolynomialFeatures` with ``degree`` equal to 0 will raise error when ``include\_bias`` is set to False, and outputs a single constant array when ``include\_bias`` is set to True. :pr:`23370` by :user:`Zhehao Liu `. :mod:`sklearn.tree` ................... - \|Fix\| Fixes performance regression with low cardinality features for :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.GradientBoostingClassifier`, and :class:`ensemble.GradientBoostingRegressor`. :pr:`23410` by :user:`Loïc Estève `. :mod:`sklearn.utils` .................... - \|Fix\| :func:`utils.class\_weight.compute\_sample\_weight` now works with sparse `y`. :pr:`23115` by :user:`kernc `. .. \_changes\_1\_1: Version 1.1.0 ============= \\May 2022\\ Minimal dependencies -------------------- Version 1.1.0 of scikit-learn requires python 3.8+, numpy 1.17.3+ and scipy 1.3.2+. Optional minimal dependency is matplotlib 3.1.2+. 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. - \|Efficiency\| :class:`cluster.KMeans` now defaults to ``algorithm="lloyd"`` instead of ``algorithm="auto"``, which was equivalent to ``algorithm="elkan"``. Lloyd's algorithm and Elkan's algorithm converge to the same solution, up to numerical rounding errors, but in general Lloyd's algorithm uses much less memory, and it is often faster. - \|Efficiency\| Fitting :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.GradientBoostingClassifier`, and :class:`ensemble.GradientBoostingRegressor` is on average 15% faster than in previous versions thanks to a new sort algorithm to find the best split. Models might be different because of a different handling of splits with tied criterion values: both the old and the new sorting algorithm are unstable sorting algorithms. :pr:`22868` by `Thomas Fan`\_. - \|Fix\| The eigenvectors initialization for :class:`cluster.SpectralClustering` and :class:`manifold.SpectralEmbedding` now samples from a Gaussian when using the `'amg'` or `'lobpcg'` solver. This change improves numerical stability of the solver, but may result in a different model. - \|Fix\| :func:`feature\_selection.f\_regression` and :func:`feature\_selection.r\_regression` will now return finite score by default instead of `np.nan` and `np.inf` for some corner case. You can use `force\_finite=False` if you really want to get non-finite values and keep the old behavior. - \|Fix\| Panda's DataFrames with all non-string columns such as a MultiIndex no longer warns when passed into an Estimator. Estimators will continue to ignore the column names in DataFrames with non-string columns. For `feature\_names\_in\_` to be defined, columns must be all strings. :pr:`22410` by `Thomas Fan`\_. - \|Fix\| :class:`preprocessing.KBinsDiscretizer` changed handling of bin edges slightly, which might result in a different encoding with the same data. - \|Fix\| :func:`calibration.calibration\_curve` changed handling of bin edges slightly, which might result in a different output curve given the same data. - \|Fix\| :class:`discriminant\_analysis.LinearDiscriminantAnalysis` now uses the correct variance-scaling coefficient which may result in different model behavior. - \|Fix\| :meth:`feature\_selection.SelectFromModel.fit` and :meth:`feature\_selection.SelectFromModel.partial\_fit` can now be called with `prefit=True`. `estimators\_` will be a deep copy of `estimator` when `prefit=True`. :pr:`23271` by :user:`Guillaume Lemaitre `. Changelog --------- .. Entries	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst	main	scikit-learn	[ -0.06712687760591507, -0.03730536624789238, -0.05284132435917854, 0.013792718760669231, 0.09963417053222656, -0.09970688074827194, 0.0056144664995372295, 0.04309708625078201, -0.04629885405302048, 0.04797467216849327, 0.0919722393155098, -0.060119882225990295, 0.03726557642221451, -0.10961...	0.11406
given the same data. - \|Fix\| :class:`discriminant\_analysis.LinearDiscriminantAnalysis` now uses the correct variance-scaling coefficient which may result in different model behavior. - \|Fix\| :meth:`feature\_selection.SelectFromModel.fit` and :meth:`feature\_selection.SelectFromModel.partial\_fit` can now be called with `prefit=True`. `estimators\_` will be a deep copy of `estimator` when `prefit=True`. :pr:`23271` by :user:`Guillaume Lemaitre `. Changelog --------- .. 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. - \|Efficiency\| Low-level routines for reductions on pairwise distances for dense float64 datasets have been refactored. The following functions and estimators now benefit from improved performances in terms of hardware scalability and speed-ups: - :func:`sklearn.metrics.pairwise\_distances\_argmin` - :func:`sklearn.metrics.pairwise\_distances\_argmin\_min` - :class:`sklearn.cluster.AffinityPropagation` - :class:`sklearn.cluster.Birch` - :class:`sklearn.cluster.MeanShift` - :class:`sklearn.cluster.OPTICS` - :class:`sklearn.cluster.SpectralClustering` - :func:`sklearn.feature\_selection.mutual\_info\_regression` - :class:`sklearn.neighbors.KNeighborsClassifier` - :class:`sklearn.neighbors.KNeighborsRegressor` - :class:`sklearn.neighbors.RadiusNeighborsClassifier` - :class:`sklearn.neighbors.RadiusNeighborsRegressor` - :class:`sklearn.neighbors.LocalOutlierFactor` - :class:`sklearn.neighbors.NearestNeighbors` - :class:`sklearn.manifold.Isomap` - :class:`sklearn.manifold.LocallyLinearEmbedding` - :class:`sklearn.manifold.TSNE` - :func:`sklearn.manifold.trustworthiness` - :class:`sklearn.semi\_supervised.LabelPropagation` - :class:`sklearn.semi\_supervised.LabelSpreading` For instance :class:`sklearn.neighbors.NearestNeighbors.kneighbors` and :class:`sklearn.neighbors.NearestNeighbors.radius\_neighbors` can respectively be up to ×20 and ×5 faster than previously on a laptop. Moreover, implementations of those two algorithms are now suitable for machine with many cores, making them usable for datasets consisting of millions of samples. :pr:`21987`, :pr:`22064`, :pr:`22065`, :pr:`22288` and :pr:`22320` by :user:`Julien Jerphanion `. - \|Enhancement\| All scikit-learn models now generate a more informative error message when some input contains unexpected `NaN` or infinite values. In particular the message contains the input name ("X", "y" or "sample\_weight") and if an unexpected `NaN` value is found in `X`, the error message suggests potential solutions. :pr:`21219` by :user:`Olivier Grisel `. - \|Enhancement\| All scikit-learn models now generate a more informative error message when setting invalid hyper-parameters with `set\_params`. :pr:`21542` by :user:`Olivier Grisel `. - \|Enhancement\| Removes random unique identifiers in the HTML representation. With this change, jupyter notebooks are reproducible as long as the cells are run in the same order. :pr:`23098` by `Thomas Fan`\_. - \|Fix\| Estimators with `non\_deterministic` tag set to `True` will skip both `check\_methods\_sample\_order\_invariance` and `check\_methods\_subset\_invariance` tests. :pr:`22318` by :user:`Zhehao Liu `. - \|API\| The option for using the log loss, aka binomial or multinomial deviance, via the `loss` parameters was made more consistent. The preferred way is by setting the value to `"log\_loss"`. Old option names are still valid and produce the same models, but are deprecated and will be removed in version 1.3. - For :class:`ensemble.GradientBoostingClassifier`, the `loss` parameter name "deviance" is deprecated in favor of the new name "log\_loss", which is now the default. :pr:`23036` by :user:`Christian Lorentzen `. - For :class:`ensemble.HistGradientBoostingClassifier`, the `loss` parameter names "auto", "binary\_crossentropy" and "categorical\_crossentropy" are deprecated in favor of the new name "log\_loss", which is now the default. :pr:`23040` by :user:`Christian Lorentzen `. - For :class:`linear\_model.SGDClassifier`, the `loss` parameter name "log" is deprecated in favor of the new name "log\_loss". :pr:`23046` by :user:`Christian Lorentzen `. - \|API\| Rich html representation of estimators is now enabled by default in Jupyter notebooks. It can be deactivated by setting `display='text'` in :func:`sklearn.set\_config`. :pr:`22856` by :user:`Jérémie du Boisberranger `. :mod:`sklearn.calibration` .......................... - \|Enhancement\| :func:`calibration.calibration\_curve` accepts a parameter `pos\_label` to specify the positive class label. :pr:`21032` by :user:`Guillaume Lemaitre `. - \|Enhancement\| :meth:`calibration.CalibratedClassifierCV.fit` now supports passing `fit\_params`, which are routed to the `base\_estimator`. :pr:`18170` by :user:`Benjamin Bossan `. - \|Enhancement\| :class:`calibration.CalibrationDisplay` accepts a parameter `pos\_label` to add this information to the plot. :pr:`21038` by :user:`Guillaume Lemaitre `. - \|Fix\| :func:`calibration.calibration\_curve` handles	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst	main	scikit-learn	[ -0.02654946967959404, -0.037657078355550766, 0.07092227041721344, 0.07849464565515518, 0.03163157030940056, 0.0074388389475643635, -0.03231019526720047, 0.06165309250354767, -0.02483936958014965, 0.02942720614373684, 0.0765368640422821, -0.058684904128313065, -0.060985367745161057, -0.0656...	0.003735
the positive class label. :pr:`21032` by :user:`Guillaume Lemaitre `. - \|Enhancement\| :meth:`calibration.CalibratedClassifierCV.fit` now supports passing `fit\_params`, which are routed to the `base\_estimator`. :pr:`18170` by :user:`Benjamin Bossan `. - \|Enhancement\| :class:`calibration.CalibrationDisplay` accepts a parameter `pos\_label` to add this information to the plot. :pr:`21038` by :user:`Guillaume Lemaitre `. - \|Fix\| :func:`calibration.calibration\_curve` handles bin edges more consistently now. :pr:`14975` by `Andreas Müller`\_ and :pr:`22526` by :user:`Meekail Zain `. - \|API\| :func:`calibration.calibration\_curve`'s `normalize` parameter is now deprecated and will be removed in version 1.3. It is recommended that a proper probability (i.e. a classifier's :term:`predict\_proba` positive class) is used for `y\_prob`. :pr:`23095` by :user:`Jordan Silke `. :mod:`sklearn.cluster` ...................... - \|MajorFeature\| :class:`cluster.BisectingKMeans` introducing Bisecting K-Means algorithm :pr:`20031` by :user:`Michal Krawczyk `, :user:`Tom Dupre la Tour ` and :user:`Jérémie du Boisberranger `. - \|Enhancement\| :class:`cluster.SpectralClustering` and :func:`cluster.spectral\_clustering` now include the new `'cluster\_qr'` method that clusters samples in the embedding space as an alternative to the existing `'kmeans'` and `'discrete'` methods. See :func:`cluster.spectral\_clustering` for more details. :pr:`21148` by :user:`Andrew Knyazev `. - \|Enhancement\| Adds :term:`get\_feature\_names\_out` to :class:`cluster.Birch`, :class:`cluster.FeatureAgglomeration`, :class:`cluster.KMeans`, :class:`cluster.MiniBatchKMeans`. :pr:`22255` by `Thomas Fan`\_. - \|Enhancement\| :class:`cluster.SpectralClustering` now raises consistent error messages when passed invalid values for `n\_clusters`, `n\_init`, `gamma`, `n\_neighbors`, `eigen\_tol` or `degree`. :pr:`21881` by :user:`Hugo Vassard `. - \|Enhancement\| :class:`cluster.AffinityPropagation` now returns cluster centers and labels if they exist, even if the model has not fully converged. When returning these potentially-degenerate cluster centers and labels, a new warning message is shown. If no cluster centers were constructed, then the cluster centers remain an empty list with labels set to `-1` and the original warning message is shown. :pr:`22217` by :user:`Meekail Zain `. - \|Efficiency\| In :class:`cluster.KMeans`, the default ``algorithm`` is now ``"lloyd"`` which is the full classical EM-style algorithm. Both ``"auto"`` and ``"full"`` are deprecated and will be removed in version 1.3. They are now aliases for ``"lloyd"``. The previous default was ``"auto"``, which relied on Elkan's algorithm. Lloyd's algorithm uses less memory than Elkan's, it is faster on many datasets, and its results are identical, hence the change. :pr:`21735` by :user:`Aurélien Geron `. - \|Fix\| :class:`cluster.KMeans`'s `init` parameter now properly supports array-like input and NumPy string scalars. :pr:`22154` by `Thomas Fan`\_. :mod:`sklearn.compose` ...................... - \|Fix\| :class:`compose.ColumnTransformer` now removes validation errors from `\_\_init\_\_` and `set\_params` methods. :pr:`22537` by :user:`iofall ` and :user:`Arisa Y. `. - \|Fix\| :term:`get\_feature\_names\_out` functionality in :class:`compose.ColumnTransformer` was broken when columns were specified using `slice`. This is fixed in :pr:`22775` and :pr:`22913` by :user:`randomgeek78 `. :mod:`sklearn.covariance` ......................... - \|Fix\| :class:`covariance.GraphicalLassoCV` now accepts NumPy array for the parameter `alphas`. :pr:`22493` by :user:`Guillaume Lemaitre `. :mod:`sklearn.cross\_decomposition` .................................. - \|Enhancement\| the `inverse\_transform` method of :class:`cross\_decomposition.PLSRegression`, :class:`cross\_decomposition.PLSCanonical` and :class:`cross\_decomposition.CCA` now allows reconstruction of a `X` target when a `Y` parameter is given. :pr:`19680` by :user:`Robin Thibaut `. - \|Enhancement\| Adds :term:`get\_feature\_names\_out` to all transformers in the :mod:`~sklearn.cross\_decomposition` module: :class:`cross\_decomposition.CCA`, :class:`cross\_decomposition.PLSSVD`, :class:`cross\_decomposition.PLSRegression`, and :class:`cross\_decomposition.PLSCanonical`. :pr:`22119` by `Thomas Fan`\_. - \|Fix\| The shape of the :term:`coef\_` attribute of :class:`cross\_decomposition.CCA`, :class:`cross\_decomposition.PLSCanonical` and :class:`cross\_decomposition.PLSRegression` will change in version 1.3, from `(n\_features, n\_targets)` to `(n\_targets, n\_features)`, to be consistent with other linear models and to make it work with interface expecting a specific shape for `coef\_` (e.g. :class:`feature\_selection.RFE`). :pr:`22016` by :user:`Guillaume Lemaitre `. - \|API\| add the fitted attribute `intercept\_` to :class:`cross\_decomposition.PLSCanonical`, :class:`cross\_decomposition.PLSRegression`, and :class:`cross\_decomposition.CCA`. The method `predict` is indeed equivalent to `Y = X @ coef\_ + intercept\_`. :pr:`22015` by :user:`Guillaume Lemaitre `. :mod:`sklearn.datasets` ....................... - \|Feature\| :func:`datasets.load\_files` now accepts an ignore list and an allow list based on file extensions. :pr:`19747` by :user:`Tony Attalla ` and :pr:`22498` by :user:`Meekail Zain `. - \|Enhancement\| :func:`datasets.make\_swiss\_roll` now supports the optional argument hole; when set to True, it returns the swiss-hole dataset.	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst	main	scikit-learn	[ -0.062381561845541, -0.003032227046787739, -0.09407199919223785, -0.04648042842745781, 0.03341493010520935, -0.03989621624350548, 0.05969759821891785, 0.05959019064903259, -0.03627819940447807, -0.0010893491562455893, 0.06924426555633545, -0.09484389424324036, 0.035673320293426514, -0.0328...	0.05461
:user:`Guillaume Lemaitre `. :mod:`sklearn.datasets` ....................... - \|Feature\| :func:`datasets.load\_files` now accepts an ignore list and an allow list based on file extensions. :pr:`19747` by :user:`Tony Attalla ` and :pr:`22498` by :user:`Meekail Zain `. - \|Enhancement\| :func:`datasets.make\_swiss\_roll` now supports the optional argument hole; when set to True, it returns the swiss-hole dataset. :pr:`21482` by :user:`Sebastian Pujalte `. - \|Enhancement\| :func:`datasets.make\_blobs` no longer copies data during the generation process, therefore uses less memory. :pr:`22412` by :user:`Zhehao Liu `. - \|Enhancement\| :func:`datasets.load\_diabetes` now accepts the parameter ``scaled``, to allow loading unscaled data. The scaled version of this dataset is now computed from the unscaled data, and can produce slightly different results than in previous version (within a 1e-4 absolute tolerance). :pr:`16605` by :user:`Mandy Gu `. - \|Enhancement\| :func:`datasets.fetch\_openml` now has two optional arguments `n\_retries` and `delay`. By default, :func:`datasets.fetch\_openml` will retry 3 times in case of a network failure with a delay between each try. :pr:`21901` by :user:`Rileran `. - \|Fix\| :func:`datasets.fetch\_covtype` is now concurrent-safe: data is downloaded to a temporary directory before being moved to the data directory. :pr:`23113` by :user:`Ilion Beyst `. - \|API\| :func:`datasets.make\_sparse\_coded\_signal` now accepts a parameter `data\_transposed` to explicitly specify the shape of matrix `X`. The default behavior `True` is to return a transposed matrix `X` corresponding to a `(n\_features, n\_samples)` shape. The default value will change to `False` in version 1.3. :pr:`21425` by :user:`Gabriel Stefanini Vicente `. :mod:`sklearn.decomposition` ............................ - \|MajorFeature\| Added a new estimator :class:`decomposition.MiniBatchNMF`. It is a faster but less accurate version of non-negative matrix factorization, better suited for large datasets. :pr:`16948` by :user:`Chiara Marmo `, :user:`Patricio Cerda ` and :user:`Jérémie du Boisberranger `. - \|Enhancement\| :func:`decomposition.dict\_learning`, :func:`decomposition.dict\_learning\_online` and :func:`decomposition.sparse\_encode` preserve dtype for `numpy.float32`. :class:`decomposition.DictionaryLearning`, :class:`decomposition.MiniBatchDictionaryLearning` and :class:`decomposition.SparseCoder` preserve dtype for `numpy.float32`. :pr:`22002` by :user:`Takeshi Oura `. - \|Enhancement\| :class:`decomposition.PCA` exposes a parameter `n\_oversamples` to tune :func:`utils.extmath.randomized\_svd` and get accurate results when the number of features is large. :pr:`21109` by :user:`Smile `. - \|Enhancement\| The :class:`decomposition.MiniBatchDictionaryLearning` and :func:`decomposition.dict\_learning\_online` have been refactored and now have a stopping criterion based on a small change of the dictionary or objective function, controlled by the new `max\_iter`, `tol` and `max\_no\_improvement` parameters. In addition, some of their parameters and attributes are deprecated. - the `n\_iter` parameter of both is deprecated. Use `max\_iter` instead. - the `iter\_offset`, `return\_inner\_stats`, `inner\_stats` and `return\_n\_iter` parameters of :func:`decomposition.dict\_learning\_online` serve internal purpose and are deprecated. - the `inner\_stats\_`, `iter\_offset\_` and `random\_state\_` attributes of :class:`decomposition.MiniBatchDictionaryLearning` serve internal purpose and are deprecated. - the default value of the `batch\_size` parameter of both will change from 3 to 256 in version 1.3. :pr:`18975` by :user:`Jérémie du Boisberranger `. - \|Enhancement\| :class:`decomposition.SparsePCA` and :class:`decomposition.MiniBatchSparsePCA` preserve dtype for `numpy.float32`. :pr:`22111` by :user:`Takeshi Oura `. - \|Enhancement\| :class:`decomposition.TruncatedSVD` now allows `n\_components == n\_features`, if `algorithm='randomized'`. :pr:`22181` by :user:`Zach Deane-Mayer `. - \|Enhancement\| Adds :term:`get\_feature\_names\_out` to all transformers in the :mod:`~sklearn.decomposition` module: :class:`decomposition.DictionaryLearning`, :class:`decomposition.FactorAnalysis`, :class:`decomposition.FastICA`, :class:`decomposition.IncrementalPCA`, :class:`decomposition.KernelPCA`, :class:`decomposition.LatentDirichletAllocation`, :class:`decomposition.MiniBatchDictionaryLearning`, :class:`decomposition.MiniBatchSparsePCA`, :class:`decomposition.NMF`, :class:`decomposition.PCA`, :class:`decomposition.SparsePCA`, and :class:`decomposition.TruncatedSVD`. :pr:`21334` by `Thomas Fan`\_. - \|Enhancement\| :class:`decomposition.TruncatedSVD` exposes the parameter `n\_oversamples` and `power\_iteration\_normalizer` to tune :func:`utils.extmath.randomized\_svd` and get accurate results when the number of features is large, the rank of the matrix is high, or other features of the matrix make low rank approximation difficult. :pr:`21705` by :user:`Jay S. Stanley III `. - \|Enhancement\| :class:`decomposition.PCA` exposes the parameter `power\_iteration\_normalizer` to tune :func:`utils.extmath.randomized\_svd` and get more accurate results when low rank approximation is difficult. :pr:`21705` by :user:`Jay S. Stanley III `. - \|Fix\| :class:`decomposition.FastICA` now validates input parameters in `fit` instead of `\_\_init\_\_`. :pr:`21432` by :user:`Hannah Bohle ` and :user:`Maren Westermann `. - \|Fix\| :class:`decomposition.FastICA` now accepts `np.float32` data without silent upcasting. The dtype is preserved by `fit` and `fit\_transform`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst	main	scikit-learn	[ -0.05972301587462425, 0.06694412976503372, -0.0753718912601471, 0.030316591262817383, 0.05736241489648819, -0.07609516382217407, 0.06499475985765457, 0.03330526500940323, -0.05922718718647957, 0.0005647989455610514, 0.09630502015352249, -0.01808549091219902, -0.011675871908664703, -0.09424...	0.025321
rank approximation is difficult. :pr:`21705` by :user:`Jay S. Stanley III `. - \|Fix\| :class:`decomposition.FastICA` now validates input parameters in `fit` instead of `\_\_init\_\_`. :pr:`21432` by :user:`Hannah Bohle ` and :user:`Maren Westermann `. - \|Fix\| :class:`decomposition.FastICA` now accepts `np.float32` data without silent upcasting. The dtype is preserved by `fit` and `fit\_transform` and the main fitted attributes use a dtype of the same precision as the training data. :pr:`22806` by :user:`Jihane Bennis ` and :user:`Olivier Grisel `. - \|Fix\| :class:`decomposition.FactorAnalysis` now validates input parameters in `fit` instead of `\_\_init\_\_`. :pr:`21713` by :user:`Haya ` and :user:`Krum Arnaudov `. - \|Fix\| :class:`decomposition.KernelPCA` now validates input parameters in `fit` instead of `\_\_init\_\_`. :pr:`21567` by :user:`Maggie Chege `. - \|Fix\| :class:`decomposition.PCA` and :class:`decomposition.IncrementalPCA` more safely calculate precision using the inverse of the covariance matrix if `self.noise\_variance\_` is zero. :pr:`22300` by :user:`Meekail Zain ` and :pr:`15948` by :user:`sysuresh`. - \|Fix\| Greatly reduced peak memory usage in :class:`decomposition.PCA` when calling `fit` or `fit\_transform`. :pr:`22553` by :user:`Meekail Zain `. - \|API\| :func:`decomposition.FastICA` now supports unit variance for whitening. The default value of its `whiten` argument will change from `True` (which behaves like `'arbitrary-variance'`) to `'unit-variance'` in version 1.3. :pr:`19490` by :user:`Facundo Ferrin ` and :user:`Julien Jerphanion `. :mod:`sklearn.discriminant\_analysis` .................................... - \|Enhancement\| Adds :term:`get\_feature\_names\_out` to :class:`discriminant\_analysis.LinearDiscriminantAnalysis`. :pr:`22120` by `Thomas Fan`\_. - \|Fix\| :class:`discriminant\_analysis.LinearDiscriminantAnalysis` now uses the correct variance-scaling coefficient which may result in different model behavior. :pr:`15984` by :user:`Okon Samuel ` and :pr:`22696` by :user:`Meekail Zain `. :mod:`sklearn.dummy` .................... - \|Fix\| :class:`dummy.DummyRegressor` no longer overrides the `constant` parameter during `fit`. :pr:`22486` by `Thomas Fan`\_. :mod:`sklearn.ensemble` ....................... - \|MajorFeature\| Added additional option `loss="quantile"` to :class:`ensemble.HistGradientBoostingRegressor` for modelling quantiles. The quantile level can be specified with the new parameter `quantile`. :pr:`21800` and :pr:`20567` by :user:`Christian Lorentzen `. - \|Efficiency\| `fit` of :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` now calls :func:`utils.check\_array` with parameter `force\_all\_finite=False` for non initial warm-start runs as it has already been checked before. :pr:`22159` by :user:`Geoffrey Paris `. - \|Enhancement\| :class:`ensemble.HistGradientBoostingClassifier` is faster, for binary and in particular for multiclass problems thanks to the new private loss function module. :pr:`20811`, :pr:`20567` and :pr:`21814` by :user:`Christian Lorentzen `. - \|Enhancement\| Adds support to use pre-fit models with `cv="prefit"` in :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor`. :pr:`16748` by :user:`Siqi He ` and :pr:`22215` by :user:`Meekail Zain `. - \|Enhancement\| :class:`ensemble.RandomForestClassifier` and :class:`ensemble.ExtraTreesClassifier` have the new `criterion="log\_loss"`, which is equivalent to `criterion="entropy"`. :pr:`23047` by :user:`Christian Lorentzen `. - \|Enhancement\| Adds :term:`get\_feature\_names\_out` to :class:`ensemble.VotingClassifier`, :class:`ensemble.VotingRegressor`, :class:`ensemble.StackingClassifier`, and :class:`ensemble.StackingRegressor`. :pr:`22695` and :pr:`22697` by `Thomas Fan`\_. - \|Enhancement\| :class:`ensemble.RandomTreesEmbedding` now has an informative :term:`get\_feature\_names\_out` function that includes both tree index and leaf index in the output feature names. :pr:`21762` by :user:`Zhehao Liu ` and `Thomas Fan`\_. - \|Efficiency\| Fitting a :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`, and :class:`ensemble.RandomTreesEmbedding` is now faster in a multiprocessing setting, especially for subsequent fits with `warm\_start` enabled. :pr:`22106` by :user:`Pieter Gijsbers `. - \|Fix\| Change the parameter `validation\_fraction` in :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` so that an error is raised if anything other than a float is passed in as an argument. :pr:`21632` by :user:`Genesis Valencia `. - \|Fix\| Removed a potential source of CPU oversubscription in :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` when CPU resource usage is limited, for instance using cgroups quota in a docker container. :pr:`22566` by :user:`Jérémie du Boisberranger `. - \|Fix\| :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` no longer warn when fitting on a pandas DataFrame with a non-default `scoring` parameter and early\_stopping enabled. :pr:`22908` by `Thomas Fan`\_. - \|Fix\| Fixes HTML repr for :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor`. :pr:`23097` by `Thomas Fan`\_. - \|API\| The attribute `loss\_` of :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` has been deprecated and will be removed in version 1.3. :pr:`23079` by :user:`Christian Lorentzen `. - \|API\| Changed the default of `max\_features`	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst	main	scikit-learn	[ -0.08097995817661285, -0.03321487829089165, -0.04670336842536926, -0.010990546084940434, 0.03343627229332924, 0.015345543622970581, 0.006666284054517746, 0.02338833548128605, -0.11727824062108994, -0.015619994141161442, 0.022368917241692543, -0.03037499077618122, 0.024212975054979324, -0.0...	0.020653
early\_stopping enabled. :pr:`22908` by `Thomas Fan`\_. - \|Fix\| Fixes HTML repr for :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor`. :pr:`23097` by `Thomas Fan`\_. - \|API\| The attribute `loss\_` of :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` has been deprecated and will be removed in version 1.3. :pr:`23079` by :user:`Christian Lorentzen `. - \|API\| Changed the default of `max\_features` to 1.0 for :class:`ensemble.RandomForestRegressor` and to `"sqrt"` for :class:`ensemble.RandomForestClassifier`. Note that these give the same fit results as before, but are much easier to understand. The old default value `"auto"` has been deprecated and will be removed in version 1.3. The same changes are also applied for :class:`ensemble.ExtraTreesRegressor` and :class:`ensemble.ExtraTreesClassifier`. :pr:`20803` by :user:`Brian Sun `. - \|Efficiency\| Improve runtime performance of :class:`ensemble.IsolationForest` by skipping repetitive input checks. :pr:`23149` by :user:`Zhehao Liu `. :mod:`sklearn.feature\_extraction` ................................. - \|Feature\| :class:`feature\_extraction.FeatureHasher` now supports PyPy. :pr:`23023` by `Thomas Fan`\_. - \|Fix\| :class:`feature\_extraction.FeatureHasher` now validates input parameters in `transform` instead of `\_\_init\_\_`. :pr:`21573` by :user:`Hannah Bohle ` and :user:`Maren Westermann `. - \|Fix\| :class:`feature\_extraction.text.TfidfVectorizer` now does not create a :class:`feature\_extraction.text.TfidfTransformer` at `\_\_init\_\_` as required by our API. :pr:`21832` by :user:`Guillaume Lemaitre `. :mod:`sklearn.feature\_selection` ................................ - \|Feature\| Added auto mode to :class:`feature\_selection.SequentialFeatureSelector`. If the argument `n\_features\_to\_select` is `'auto'`, select features until the score improvement does not exceed the argument `tol`. The default value of `n\_features\_to\_select` changed from `None` to `'warn'` in 1.1 and will become `'auto'` in 1.3. `None` and `'warn'` will be removed in 1.3. :pr:`20145` by :user:`murata-yu `. - \|Feature\| Added the ability to pass callables to the `max\_features` parameter of :class:`feature\_selection.SelectFromModel`. Also introduced new attribute `max\_features\_` which is inferred from `max\_features` and the data during `fit`. If `max\_features` is an integer, then `max\_features\_ = max\_features`. If `max\_features` is a callable, then `max\_features\_ = max\_features(X)`. :pr:`22356` by :user:`Meekail Zain `. - \|Enhancement\| :class:`feature\_selection.GenericUnivariateSelect` preserves float32 dtype. :pr:`18482` by :user:`Thierry Gameiro ` and :user:`Daniel Kharsa ` and :pr:`22370` by :user:`Meekail Zain `. - \|Enhancement\| Add a parameter `force\_finite` to :func:`feature\_selection.f\_regression` and :func:`feature\_selection.r\_regression`. This parameter allows to force the output to be finite in the case where a feature or the target is constant or that the feature and target are perfectly correlated (only for the F-statistic). :pr:`17819` by :user:`Juan Carlos Alfaro Jiménez `. - \|Efficiency\| Improve runtime performance of :func:`feature\_selection.chi2` with boolean arrays. :pr:`22235` by `Thomas Fan`\_. - \|Efficiency\| Reduced memory usage of :func:`feature\_selection.chi2`. :pr:`21837` by :user:`Louis Wagner `. :mod:`sklearn.gaussian\_process` ............................... - \|Fix\| `predict` and `sample\_y` methods of :class:`gaussian\_process.GaussianProcessRegressor` now return arrays of the correct shape in single-target and multi-target cases, and for both `normalize\_y=False` and `normalize\_y=True`. :pr:`22199` by :user:`Guillaume Lemaitre `, :user:`Aidar Shakerimoff ` and :user:`Tenavi Nakamura-Zimmerer `. - \|Fix\| :class:`gaussian\_process.GaussianProcessClassifier` raises a more informative error if `CompoundKernel` is passed via `kernel`. :pr:`22223` by :user:`MarcoM `. :mod:`sklearn.impute` ..................... - \|Enhancement\| :class:`impute.SimpleImputer` now warns with feature names when features which are skipped due to the lack of any observed values in the training set. :pr:`21617` by :user:`Christian Ritter `. - \|Enhancement\| Added support for `pd.NA` in :class:`impute.SimpleImputer`. :pr:`21114` by :user:`Ying Xiong `. - \|Enhancement\| Adds :term:`get\_feature\_names\_out` to :class:`impute.SimpleImputer`, :class:`impute.KNNImputer`, :class:`impute.IterativeImputer`, and :class:`impute.MissingIndicator`. :pr:`21078` by `Thomas Fan`\_. - \|API\| The `verbose` parameter was deprecated for :class:`impute.SimpleImputer`. A warning will always be raised upon the removal of empty columns. :pr:`21448` by :user:`Oleh Kozynets ` and :user:`Christian Ritter `. :mod:`sklearn.inspection` ......................... - \|Feature\| Add a display to plot the boundary decision of a classifier by using the method :func:`inspection.DecisionBoundaryDisplay.from\_estimator`. :pr:`16061` by `Thomas Fan`\_. - \|Enhancement\| In :meth:`inspection.PartialDependenceDisplay.from\_estimator`, allow `kind` to accept a list of strings to specify which type of plot to draw for each feature interaction. :pr:`19438` by :user:`Guillaume Lemaitre `. - \|Enhancement\| :meth:`inspection.PartialDependenceDisplay.from\_estimator`, :meth:`inspection.PartialDependenceDisplay.plot`, and `inspection.plot\_partial\_dependence` now support plotting centered Individual Conditional Expectation (cICE) and centered PDP	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst	main	scikit-learn	[ -0.1467249095439911, -0.08982867002487183, 0.006367791444063187, 0.06788715720176697, 0.03760605305433273, 0.06244032829999924, -0.07500189542770386, -0.01371717732399702, -0.050934940576553345, 0.00018395745428279042, -0.021019889041781425, -0.037460651248693466, -0.0050378101877868176, -...	0.02858
:pr:`16061` by `Thomas Fan`\_. - \|Enhancement\| In :meth:`inspection.PartialDependenceDisplay.from\_estimator`, allow `kind` to accept a list of strings to specify which type of plot to draw for each feature interaction. :pr:`19438` by :user:`Guillaume Lemaitre `. - \|Enhancement\| :meth:`inspection.PartialDependenceDisplay.from\_estimator`, :meth:`inspection.PartialDependenceDisplay.plot`, and `inspection.plot\_partial\_dependence` now support plotting centered Individual Conditional Expectation (cICE) and centered PDP curves controlled by setting the parameter `centered`. :pr:`18310` by :user:`Johannes Elfner ` and :user:`Guillaume Lemaitre `. :mod:`sklearn.isotonic` ....................... - \|Enhancement\| Adds :term:`get\_feature\_names\_out` to :class:`isotonic.IsotonicRegression`. :pr:`22249` by `Thomas Fan`\_. :mod:`sklearn.kernel\_approximation` ................................... - \|Enhancement\| Adds :term:`get\_feature\_names\_out` to :class:`kernel\_approximation.AdditiveChi2Sampler`. :class:`kernel\_approximation.Nystroem`, :class:`kernel\_approximation.PolynomialCountSketch`, :class:`kernel\_approximation.RBFSampler`, and :class:`kernel\_approximation.SkewedChi2Sampler`. :pr:`22137` and :pr:`22694` by `Thomas Fan`\_. :mod:`sklearn.linear\_model` ........................... - \|Feature\| :class:`linear\_model.ElasticNet`, :class:`linear\_model.ElasticNetCV`, :class:`linear\_model.Lasso` and :class:`linear\_model.LassoCV` support `sample\_weight` for sparse input `X`. :pr:`22808` by :user:`Christian Lorentzen `. - \|Feature\| :class:`linear\_model.Ridge` with `solver="lsqr"` now supports to fit sparse input with `fit\_intercept=True`. :pr:`22950` by :user:`Christian Lorentzen `. - \|Enhancement\| :class:`linear\_model.QuantileRegressor` support sparse input for the highs based solvers. :pr:`21086` by :user:`Venkatachalam Natchiappan `. In addition, those solvers now use the CSC matrix right from the beginning which speeds up fitting. :pr:`22206` by :user:`Christian Lorentzen `. - \|Enhancement\| :class:`linear\_model.LogisticRegression` is faster for ``solvers="lbfgs"`` and ``solver="newton-cg"``, for binary and in particular for multiclass problems thanks to the new private loss function module. In the multiclass case, the memory consumption has also been reduced for these solvers as the target is now label encoded (mapped to integers) instead of label binarized (one-hot encoded). The more classes, the larger the benefit. :pr:`21808`, :pr:`20567` and :pr:`21814` by :user:`Christian Lorentzen `. - \|Enhancement\| :class:`linear\_model.GammaRegressor`, :class:`linear\_model.PoissonRegressor` and :class:`linear\_model.TweedieRegressor` are faster for ``solvers="lbfgs"``. :pr:`22548`, :pr:`21808` and :pr:`20567` by :user:`Christian Lorentzen `. - \|Enhancement\| Rename parameter `base\_estimator` to `estimator` in :class:`linear\_model.RANSACRegressor` to improve readability and consistency. `base\_estimator` is deprecated and will be removed in 1.3. :pr:`22062` by :user:`Adrian Trujillo `. - \|Enhancement\| :func:`linear\_model.ElasticNet` and other linear model classes using coordinate descent show error messages when non-finite parameter weights are produced. :pr:`22148` by :user:`Christian Ritter ` and :user:`Norbert Preining `. - \|Enhancement\| :class:`linear\_model.ElasticNet` and :class:`linear\_model.Lasso` now raise consistent error messages when passed invalid values for `l1\_ratio`, `alpha`, `max\_iter` and `tol`. :pr:`22240` by :user:`Arturo Amor `. - \|Enhancement\| :class:`linear\_model.BayesianRidge` and :class:`linear\_model.ARDRegression` now preserve float32 dtype. :pr:`9087` by :user:`Arthur Imbert ` and :pr:`22525` by :user:`Meekail Zain `. - \|Enhancement\| :class:`linear\_model.RidgeClassifier` is now supporting multilabel classification. :pr:`19689` by :user:`Guillaume Lemaitre `. - \|Enhancement\| :class:`linear\_model.RidgeCV` and :class:`linear\_model.RidgeClassifierCV` now raise consistent error message when passed invalid values for `alphas`. :pr:`21606` by :user:`Arturo Amor `. - \|Enhancement\| :class:`linear\_model.Ridge` and :class:`linear\_model.RidgeClassifier` now raise consistent error message when passed invalid values for `alpha`, `max\_iter` and `tol`. :pr:`21341` by :user:`Arturo Amor `. - \|Enhancement\| :func:`linear\_model.orthogonal\_mp\_gram` preserves dtype for `numpy.float32`. :pr:`22002` by :user:`Takeshi Oura `. - \|Fix\| :class:`linear\_model.LassoLarsIC` now correctly computes AIC and BIC. An error is now raised when `n\_features > n\_samples` and when the noise variance is not provided. :pr:`21481` by :user:`Guillaume Lemaitre ` and :user:`Andrés Babino `. - \|Fix\| :class:`linear\_model.TheilSenRegressor` now validates input parameter ``max\_subpopulation`` in `fit` instead of `\_\_init\_\_`. :pr:`21767` by :user:`Maren Westermann `. - \|Fix\| :class:`linear\_model.ElasticNetCV` now produces correct warning when `l1\_ratio=0`. :pr:`21724` by :user:`Yar Khine Phyo `. - \|Fix\| :class:`linear\_model.LogisticRegression` and :class:`linear\_model.LogisticRegressionCV` now set the `n\_iter\_` attribute with a shape that respects the docstring and that is consistent with the shape obtained when using the other solvers in the one-vs-rest setting. Previously, it would record only the maximum of the number of iterations for each binary sub-problem while now all of them are recorded. :pr:`21998` by :user:`Olivier Grisel `. - \|Fix\| The property `family` of :class:`linear\_model.TweedieRegressor` is not validated in `\_\_init\_\_` anymore. Instead, this (private) property is deprecated in :class:`linear\_model.GammaRegressor`, :class:`linear\_model.PoissonRegressor` and :class:`linear\_model.TweedieRegressor`, and will be removed in 1.3. :pr:`22548` by :user:`Christian Lorentzen `. - \|Fix\|	https://github.com/scikit-learn/scikit-learn/blob/main//doc/whats_new/v1.1.rst	main	scikit-learn	[ -0.01639806665480137, -0.04729152098298073, 0.03608206287026405, -0.06349588930606842, 0.027648193761706352, -0.0051076775416731834, 0.0176274161785841, 0.09201694279909134, -0.05305434390902519, 0.024106688797473907, 0.05757013335824013, -0.045461688190698624, -0.00269115692935884, -0.022...	0.063612

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