doc/whats_new/v0.19.rst
.. include:: _contributors.rst
.. currentmodule:: sklearn
.. _changes_0_19:
July, 2018
This release is exclusively in order to support Python 3.7.
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_.October 23, 2017
This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.19.0.
Note there may be minor differences in TSNE output in this release (due to
:issue:9623), in the case where multiple samples have equal distance to some
sample.
API changes ...........
Reverted the addition of metrics.ndcg_score and metrics.dcg_score
which had been merged into version 0.19.0 by error. The implementations
were broken and undocumented.
return_train_score which was added to
:class:model_selection.GridSearchCV,
:class:model_selection.RandomizedSearchCV and
:func:model_selection.cross_validate in version 0.19.0 will be changing its
default value from True to False in version 0.21. We found that calculating
training score could have a great effect on cross validation runtime in some
cases. Users should explicitly set return_train_score to False if
prediction or scoring functions are slow, resulting in a deleterious effect
on CV runtime, or to True if they wish to use the calculated scores.
:issue:9677 by :user:Kumar Ashutosh <thechargedneutron> and Joel Nothman_.
correlation_models and regression_models from the legacy gaussian
processes implementation have been belatedly deprecated. :issue:9717 by
:user:Kumar Ashutosh <thechargedneutron>.
Bug fixes .........
Avoid integer overflows in :func:metrics.matthews_corrcoef.
:issue:9693 by :user:Sam Steingold <sam-s>.
Fixed a bug in the objective function for :class:manifold.TSNE (both exact
and with the Barnes-Hut approximation) when n_components >= 3.
:issue:9711 by :user:goncalo-rodrigues.
Fix regression in :func:model_selection.cross_val_predict where it
raised an error with method='predict_proba' for some probabilistic
classifiers. :issue:9641 by :user:James Bourbeau <jrbourbeau>.
Fixed a bug where :func:datasets.make_classification modified its input
weights. :issue:9865 by :user:Sachin Kelkar <s4chin>.
:class:model_selection.StratifiedShuffleSplit now works with multioutput
multiclass or multilabel data with more than 1000 columns. :issue:9922 by
:user:Charlie Brummitt <crbrummitt>.
Fixed a bug with nested and conditional parameter setting, e.g. setting a
pipeline step and its parameter at the same time. :issue:9945 by Andreas Müller_ and Joel Nothman_.
Regressions in 0.19.0 fixed in 0.19.1:
Fixed a bug where parallelised prediction in random forests was not
thread-safe and could (rarely) result in arbitrary errors. :issue:9830 by
Joel Nothman_.
Fix regression in :func:model_selection.cross_val_predict where it no
longer accepted X as a list. :issue:9600 by :user:Rasul Kerimov <CoderINusE>.
Fixed handling of :func:model_selection.cross_val_predict for binary
classification with method='decision_function'. :issue:9593 by
:user:Reiichiro Nakano <reiinakano> and core devs.
Fix regression in :class:pipeline.Pipeline where it no longer accepted
steps as a tuple. :issue:9604 by :user:Joris Van den Bossche <jorisvandenbossche>.
Fix bug where n_iter was not properly deprecated, leaving n_iter
unavailable for interim use in
:class:linear_model.SGDClassifier, :class:linear_model.SGDRegressor,
:class:linear_model.PassiveAggressiveClassifier,
:class:linear_model.PassiveAggressiveRegressor and
:class:linear_model.Perceptron. :issue:9558 by Andreas Müller_.
Dataset fetchers make sure temporary files are closed before removing them,
which caused errors on Windows. :issue:9847 by :user:Joan Massich <massich>.
Fixed a regression in :class:manifold.TSNE where it no longer supported
metrics other than 'euclidean' and 'precomputed'. :issue:9623 by :user:Oli Blum <oliblum90>.
Enhancements ............
Our test suite and :func:utils.estimator_checks.check_estimator can now be
run without Nose installed. :issue:9697 by :user:Joan Massich <massich>.
To improve usability of version 0.19's :class:pipeline.Pipeline
caching, memory now allows joblib.Memory instances.
This make use of the new :func:utils.validation.check_memory helper.
:issue:9584 by :user:Kumar Ashutosh <thechargedneutron>
Some fixes to examples: :issue:9750, :issue:9788, :issue:9815
Made a FutureWarning in SGD-based estimators less verbose. :issue:9802 by
:user:Vrishank Bhardwaj <vrishank97>.
With thanks to:
Joel Nothman, Loic Esteve, Andreas Mueller, Kumar Ashutosh, Vrishank Bhardwaj, Hanmin Qin, Rasul Kerimov, James Bourbeau, Nagarjuna Kumar, Nathaniel Saul, Olivier Grisel, Roman Yurchak, Reiichiro Nakano, Sachin Kelkar, Sam Steingold, Yaroslav Halchenko, diegodlh, felix, goncalo-rodrigues, jkleint, oliblum90, pasbi, Anthony Gitter, Ben Lawson, Charlie Brummitt, Didi Bar-Zev, Gael Varoquaux, Joan Massich, Joris Van den Bossche, nielsenmarkus11
August 12, 2017
We are excited to release a number of great new features including
:class:neighbors.LocalOutlierFactor for anomaly detection,
:class:preprocessing.QuantileTransformer for robust feature transformation,
and the :class:multioutput.ClassifierChain meta-estimator to simply account
for dependencies between classes in multilabel problems. We have some new
algorithms in existing estimators, such as multiplicative update in
:class:decomposition.NMF and multinomial
:class:linear_model.LogisticRegression with L1 loss (use solver='saga').
Cross validation is now able to return the results from multiple metric
evaluations. The new :func:model_selection.cross_validate can return many
scores on the test data as well as training set performance and timings, and we
have extended the scoring and refit parameters for grid/randomized
search :ref:to handle multiple metrics <multimetric_grid_search>.
You can also learn faster. For instance, the :ref:new option to cache transformations <pipeline_cache> in :class:pipeline.Pipeline makes grid
search over pipelines including slow transformations much more efficient. And
you can predict faster: if you're sure you know what you're doing, you can turn
off validating that the input is finite using :func:config_context.
We've made some important fixes too. We've fixed a longstanding implementation
error in :func:metrics.average_precision_score, so please be cautious with
prior results reported from that function. A number of errors in the
:class:manifold.TSNE implementation have been fixed, particularly in the
default Barnes-Hut approximation. :class:semi_supervised.LabelSpreading and
:class:semi_supervised.LabelPropagation have had substantial fixes.
LabelPropagation was previously broken. LabelSpreading should now correctly
respect its alpha parameter.
The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.
cluster.KMeans with sparse X and initial centroids given (bug fix)cross_decomposition.PLSRegression
with scale=True (bug fix)ensemble.GradientBoostingClassifier and
:class:ensemble.GradientBoostingRegressor where min_impurity_split is used (bug fix)loss='quantile' (bug fix)ensemble.IsolationForest (bug fix)feature_selection.SelectFdr (bug fix)linear_model.RANSACRegressor (bug fix)linear_model.LassoLars (bug fix)linear_model.LassoLarsIC (bug fix)manifold.TSNE (bug fix)neighbors.NearestCentroid (bug fix)semi_supervised.LabelSpreading (bug fix)semi_supervised.LabelPropagation (bug fix)min_weight_fraction_leaf is used (enhancement)model_selection.StratifiedKFold with shuffle=True
(this change, due to :issue:7823 was not mentioned in the release notes at
the time)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.)
New features ............
Classifiers and regressors
Added :class:multioutput.ClassifierChain for multi-label
classification. By :user:Adam Kleczewski <adamklec>.
Added solver 'saga' that implements the improved version of Stochastic
Average Gradient, in :class:linear_model.LogisticRegression and
:class:linear_model.Ridge. It allows the use of L1 penalty with
multinomial logistic loss, and behaves marginally better than 'sag'
during the first epochs of ridge and logistic regression.
:issue:8446 by Arthur Mensch_.
Other estimators
Added the :class:neighbors.LocalOutlierFactor class for anomaly
detection based on nearest neighbors.
:issue:5279 by Nicolas Goix_ and Alexandre Gramfort_.
Added :class:preprocessing.QuantileTransformer class and
:func:preprocessing.quantile_transform function for features
normalization based on quantiles.
:issue:8363 by :user:Denis Engemann <dengemann>,
:user:Guillaume Lemaitre <glemaitre>, Olivier Grisel, Raghav RV,
:user:Thierry Guillemot <tguillemot>, and Gael Varoquaux_.
The new solver 'mu' implements a Multiplicate Update in
:class:decomposition.NMF, allowing the optimization of all
beta-divergences, including the Frobenius norm, the generalized
Kullback-Leibler divergence and the Itakura-Saito divergence.
:issue:5295 by Tom Dupre la Tour_.
Model selection and evaluation
:class:model_selection.GridSearchCV and
:class:model_selection.RandomizedSearchCV now support simultaneous
evaluation of multiple metrics. Refer to the
:ref:multimetric_grid_search section of the user guide for more
information. :issue:7388 by Raghav RV_
Added the :func:model_selection.cross_validate which allows evaluation
of multiple metrics. This function returns a dict with more useful
information from cross-validation such as the train scores, fit times and
score times.
Refer to :ref:multimetric_cross_validation section of the userguide
for more information. :issue:7388 by Raghav RV_
Added :func:metrics.mean_squared_log_error, which computes
the mean square error of the logarithmic transformation of targets,
particularly useful for targets with an exponential trend.
:issue:7655 by :user:Karan Desai <karandesai-96>.
Added :func:metrics.dcg_score and :func:metrics.ndcg_score, which
compute Discounted cumulative gain (DCG) and Normalized discounted
cumulative gain (NDCG).
:issue:7739 by :user:David Gasquez <davidgasquez>.
Added the :class:model_selection.RepeatedKFold and
:class:model_selection.RepeatedStratifiedKFold.
:issue:8120 by Neeraj Gangwar_.
Miscellaneous
Validation that input data contains no NaN or inf can now be suppressed
using :func:config_context, at your own risk. This will save on runtime,
and may be particularly useful for prediction time. :issue:7548 by
Joel Nothman_.
Added a test to ensure parameter listing in docstrings matches the
function/class signature. :issue:9206 by Alexandre Gramfort_ and
Raghav RV_.
Enhancements ............
Trees and ensembles
The min_weight_fraction_leaf constraint in tree construction is now
more efficient, taking a fast path to declare a node a leaf if its weight
is less than 2 * the minimum. Note that the constructed tree will be
different from previous versions where min_weight_fraction_leaf is
used. :issue:7441 by :user:Nelson Liu <nelson-liu>.
:class:ensemble.GradientBoostingClassifier and :class:ensemble.GradientBoostingRegressor
now support sparse input for prediction.
:issue:6101 by :user:Ibraim Ganiev <olologin>.
:class:ensemble.VotingClassifier now allows changing estimators by using
:meth:ensemble.VotingClassifier.set_params. An estimator can also be
removed by setting it to None.
:issue:7674 by :user:Yichuan Liu <yl565>.
:func:tree.export_graphviz now shows configurable number of decimal
places. :issue:8698 by :user:Guillaume Lemaitre <glemaitre>.
Added flatten_transform parameter to :class:ensemble.VotingClassifier
to change output shape of transform method to 2 dimensional.
:issue:7794 by :user:Ibraim Ganiev <olologin> and
:user:Herilalaina Rakotoarison <herilalaina>.
Linear, kernelized and related models
:class:linear_model.SGDClassifier, :class:linear_model.SGDRegressor,
:class:linear_model.PassiveAggressiveClassifier,
:class:linear_model.PassiveAggressiveRegressor and
:class:linear_model.Perceptron now expose max_iter and
tol parameters, to handle convergence more precisely.
n_iter parameter is deprecated, and the fitted estimator exposes
a n_iter_ attribute, with actual number of iterations before
convergence. :issue:5036 by Tom Dupre la Tour_.
Added average parameter to perform weight averaging in
:class:linear_model.PassiveAggressiveClassifier. :issue:4939
by :user:Andrea Esuli <aesuli>.
:class:linear_model.RANSACRegressor no longer throws an error
when calling fit if no inliers are found in its first iteration.
Furthermore, causes of skipped iterations are tracked in newly added
attributes, n_skips_*.
:issue:7914 by :user:Michael Horrell <mthorrell>.
In :class:gaussian_process.GaussianProcessRegressor, method predict
is a lot faster with return_std=True. :issue:8591 by
:user:Hadrien Bertrand <hbertrand>.
Added return_std to predict method of
:class:linear_model.ARDRegression and
:class:linear_model.BayesianRidge.
:issue:7838 by :user:Sergey Feldman <sergeyf>.
Memory usage enhancements: Prevent cast from float32 to float64 in:
:class:linear_model.MultiTaskElasticNet;
:class:linear_model.LogisticRegression when using newton-cg solver; and
:class:linear_model.Ridge when using svd, sparse_cg, cholesky or lsqr
solvers. :issue:8835, :issue:8061 by :user:Joan Massich <massich> and :user:Nicolas Cordier <ncordier> and :user:Thierry Guillemot <tguillemot>.
Other predictors
Custom metrics for the :mod:sklearn.neighbors binary trees now have
fewer constraints: they must take two 1d-arrays and return a float.
:issue:6288 by Jake Vanderplas_.
algorithm='auto in :mod:sklearn.neighbors estimators now chooses the most
appropriate algorithm for all input types and metrics. :issue:9145 by
:user:Herilalaina Rakotoarison <herilalaina> and :user:Reddy Chinthala <preddy5>.
Decomposition, manifold learning and clustering
:class:cluster.MiniBatchKMeans and :class:cluster.KMeans
now use significantly less memory when assigning data points to their
nearest cluster center. :issue:7721 by :user:Jon Crall <Erotemic>.
:class:decomposition.PCA, :class:decomposition.IncrementalPCA and
:class:decomposition.TruncatedSVD now expose the singular values
from the underlying SVD. They are stored in the attribute
singular_values_, like in :class:decomposition.IncrementalPCA.
:issue:7685 by :user:Tommy Löfstedt <tomlof>
:class:decomposition.NMF now faster when beta_loss=0.
:issue:9277 by :user:hongkahjun.
Memory improvements for method barnes_hut in :class:manifold.TSNE
:issue:7089 by :user:Thomas Moreau <tomMoral> and Olivier Grisel_.
Optimization schedule improvements for Barnes-Hut :class:manifold.TSNE
so the results are closer to the one from the reference implementation
lvdmaaten/bhtsne <https://github.com/lvdmaaten/bhtsne>_ by :user:Thomas Moreau <tomMoral> and Olivier Grisel_.
Memory usage enhancements: Prevent cast from float32 to float64 in
:class:decomposition.PCA and
decomposition.randomized_svd_low_rank.
:issue:9067 by Raghav RV_.
Preprocessing and feature selection
Added norm_order parameter to :class:feature_selection.SelectFromModel
to enable selection of the norm order when coef_ is more than 1D.
:issue:6181 by :user:Antoine Wendlinger <antoinewdg>.
Added ability to use sparse matrices in :func:feature_selection.f_regression
with center=True. :issue:8065 by :user:Daniel LeJeune <acadiansith>.
Small performance improvement to n-gram creation in
:mod:sklearn.feature_extraction.text by binding methods for loops and
special-casing unigrams. :issue:7567 by :user:Jaye Doepke <jtdoepke>
Relax assumption on the data for the
:class:kernel_approximation.SkewedChi2Sampler. Since the Skewed-Chi2
kernel is defined on the open interval :math:(-skewedness; +\infty)^d,
the transform function should not check whether X < 0 but whether X < -self.skewedness. :issue:7573 by :user:Romain Brault <RomainBrault>.
Made default kernel parameters kernel-dependent in
:class:kernel_approximation.Nystroem.
:issue:5229 by :user:Saurabh Bansod <mth4saurabh> and Andreas Müller_.
Model evaluation and meta-estimators
:class:pipeline.Pipeline is now able to cache transformers
within a pipeline by using the memory constructor parameter.
:issue:7990 by :user:Guillaume Lemaitre <glemaitre>.
:class:pipeline.Pipeline steps can now be accessed as attributes of its
named_steps attribute. :issue:8586 by :user:Herilalaina Rakotoarison <herilalaina>.
Added sample_weight parameter to :meth:pipeline.Pipeline.score.
:issue:7723 by :user:Mikhail Korobov <kmike>.
Added ability to set n_jobs parameter to :func:pipeline.make_union.
A TypeError will be raised for any other kwargs. :issue:8028
by :user:Alexander Booth <alexandercbooth>.
:class:model_selection.GridSearchCV,
:class:model_selection.RandomizedSearchCV and
:func:model_selection.cross_val_score now allow estimators with callable
kernels which were previously prohibited.
:issue:8005 by Andreas Müller_ .
:func:model_selection.cross_val_predict now returns output of the
correct shape for all values of the argument method.
:issue:7863 by :user:Aman Dalmia <dalmia>.
Added shuffle and random_state parameters to shuffle training
data before taking prefixes of it based on training sizes in
:func:model_selection.learning_curve.
:issue:7506 by :user:Narine Kokhlikyan <NarineK>.
:class:model_selection.StratifiedShuffleSplit now works with multioutput
multiclass (or multilabel) data. :issue:9044 by Vlad Niculae_.
Speed improvements to :class:model_selection.StratifiedShuffleSplit.
:issue:5991 by :user:Arthur Mensch <arthurmensch> and Joel Nothman_.
Add shuffle parameter to :func:model_selection.train_test_split.
:issue:8845 by :user:themrmax <themrmax>
:class:multioutput.MultiOutputRegressor and :class:multioutput.MultiOutputClassifier
now support online learning using partial_fit.
:issue: 8053 by :user:Peng Yu <yupbank>.
Add max_train_size parameter to :class:model_selection.TimeSeriesSplit
:issue:8282 by :user:Aman Dalmia <dalmia>.
More clustering metrics are now available through :func:metrics.get_scorer
and scoring parameters. :issue:8117 by Raghav RV_.
A scorer based on :func:metrics.explained_variance_score is also available.
:issue:9259 by :user:Hanmin Qin <qinhanmin2014>.
Metrics
:func:metrics.matthews_corrcoef now supports multiclass classification.
:issue:8094 by :user:Jon Crall <Erotemic>.
Add sample_weight parameter to :func:metrics.cohen_kappa_score.
:issue:8335 by :user:Victor Poughon <vpoughon>.
Miscellaneous
:func:utils.estimator_checks.check_estimator now attempts to ensure that methods
transform, predict, etc. do not set attributes on the estimator.
:issue:7533 by :user:Ekaterina Krivich <kiote>.
Added type checking to the accept_sparse parameter in
:mod:sklearn.utils.validation methods. This parameter now accepts only boolean,
string, or list/tuple of strings. accept_sparse=None is deprecated and
should be replaced by accept_sparse=False.
:issue:7880 by :user:Josh Karnofsky <jkarno>.
Make it possible to load a chunk of an svmlight formatted file by
passing a range of bytes to :func:datasets.load_svmlight_file.
:issue:935 by :user:Olivier Grisel <ogrisel>.
:class:dummy.DummyClassifier and :class:dummy.DummyRegressor
now accept non-finite features. :issue:8931 by :user:Attractadore.
Bug fixes .........
Trees and ensembles
Fixed a memory leak in trees when using trees with criterion='mae'.
:issue:8002 by Raghav RV_.
Fixed a bug where :class:ensemble.IsolationForest uses an
incorrect formula for the average path length
:issue:8549 by Peter Wang <https://github.com/PTRWang>_.
Fixed a bug where :class:ensemble.AdaBoostClassifier throws
ZeroDivisionError while fitting data with single class labels.
:issue:7501 by :user:Dominik Krzeminski <dokato>.
Fixed a bug in :class:ensemble.GradientBoostingClassifier and
:class:ensemble.GradientBoostingRegressor where a float being compared
to 0.0 using == caused a divide by zero error. :issue:7970 by
:user:He Chen <chenhe95>.
Fix a bug where :class:ensemble.GradientBoostingClassifier and
:class:ensemble.GradientBoostingRegressor ignored the
min_impurity_split parameter.
:issue:8006 by :user:Sebastian Pölsterl <sebp>.
Fixed oob_score in :class:ensemble.BaggingClassifier.
:issue:8936 by :user:Michael Lewis <mlewis1729>
Fixed excessive memory usage in prediction for random forests estimators.
:issue:8672 by :user:Mike Benfield <mikebenfield>.
Fixed a bug where sample_weight as a list broke random forests in Python 2
:issue:8068 by :user:xor.
Fixed a bug where :class:ensemble.IsolationForest fails when
max_features is less than 1.
:issue:5732 by :user:Ishank Gulati <IshankGulati>.
Fix a bug where gradient boosting with loss='quantile' computed
negative errors for negative values of ytrue - ypred leading to wrong
values when calling __call__.
:issue:8087 by :user:Alexis Mignon <AlexisMignon>
Fix a bug where :class:ensemble.VotingClassifier raises an error
when a numpy array is passed in for weights. :issue:7983 by
:user:Vincent Pham <vincentpham1991>.
Fixed a bug where :func:tree.export_graphviz raised an error
when the length of features_names does not match n_features in the decision
tree. :issue:8512 by :user:Li Li <aikinogard>.
Linear, kernelized and related models
Fixed a bug where :func:linear_model.RANSACRegressor.fit may run until
max_iter if it finds a large inlier group early. :issue:8251 by
:user:aivision2020.
Fixed a bug where :class:naive_bayes.MultinomialNB and
:class:naive_bayes.BernoulliNB failed when alpha=0. :issue:5814 by
:user:Yichuan Liu <yl565> and :user:Herilalaina Rakotoarison <herilalaina>.
Fixed a bug where :class:linear_model.LassoLars does not give
the same result as the LassoLars implementation available
in R (lars library). :issue:7849 by :user:Jair Montoya Martinez <jmontoyam>.
Fixed a bug in linear_model.RandomizedLasso,
:class:linear_model.Lars, :class:linear_model.LassoLars,
:class:linear_model.LarsCV and :class:linear_model.LassoLarsCV,
where the parameter precompute was not used consistently across
classes, and some values proposed in the docstring could raise errors.
:issue:5359 by Tom Dupre la Tour_.
Fix inconsistent results between :class:linear_model.RidgeCV and
:class:linear_model.Ridge when using normalize=True. :issue:9302
by Alexandre Gramfort_.
Fix a bug where :func:linear_model.LassoLars.fit sometimes
left coef_ as a list, rather than an ndarray.
:issue:8160 by :user:CJ Carey <perimosocordiae>.
Fix :func:linear_model.BayesianRidge.fit to return
ridge parameter alpha_ and lambda_ consistent with calculated
coefficients coef_ and intercept_.
:issue:8224 by :user:Peter Gedeck <gedeck>.
Fixed a bug in :class:svm.OneClassSVM where it returned floats instead of
integer classes. :issue:8676 by :user:Vathsala Achar <VathsalaAchar>.
Fix AIC/BIC criterion computation in :class:linear_model.LassoLarsIC.
:issue:9022 by Alexandre Gramfort_ and :user:Mehmet Basbug <mehmetbasbug>.
Fixed a memory leak in our LibLinear implementation. :issue:9024 by
:user:Sergei Lebedev <superbobry>
Fix bug where stratified CV splitters did not work with
:class:linear_model.LassoCV. :issue:8973 by
:user:Paulo Haddad <paulochf>.
Fixed a bug in :class:gaussian_process.GaussianProcessRegressor
when the standard deviation and covariance predicted without fit
would fail with a meaningless error by default.
:issue:6573 by :user:Quazi Marufur Rahman <qmaruf> and
Manoj Kumar_.
Other predictors
semi_supervised.BaseLabelPropagation to correctly implement
LabelPropagation and LabelSpreading as done in the referenced
papers. :issue:9239
by :user:Andre Ambrosio Boechat <boechat107>, :user:Utkarsh Upadhyay <musically-ut>, and Joel Nothman_.Decomposition, manifold learning and clustering
Fixed the implementation of :class:manifold.TSNE:
early_exaggeration parameter had no effect and is now used for the
first 250 optimization iterations.
Fixed the AssertionError: Tree consistency failed exception
reported in :issue:8992.
Improve the learning schedule to match the one from the reference
implementation lvdmaaten/bhtsne <https://github.com/lvdmaaten/bhtsne>.
by :user:Thomas Moreau <tomMoral> and Olivier Grisel.
Fix a bug in :class:decomposition.LatentDirichletAllocation
where the perplexity method was returning incorrect results because
the transform method returns normalized document topic distributions
as of version 0.18. :issue:7954 by :user:Gary Foreman <garyForeman>.
Fix output shape and bugs with n_jobs > 1 in
:class:decomposition.SparseCoder transform and
:func:decomposition.sparse_encode
for one-dimensional data and one component.
This also impacts the output shape of :class:decomposition.DictionaryLearning.
:issue:8086 by Andreas Müller_.
Fixed the implementation of explained_variance_
in :class:decomposition.PCA,
decomposition.RandomizedPCA and
:class:decomposition.IncrementalPCA.
:issue:9105 by Hanmin Qin <https://github.com/qinhanmin2014>_.
Fixed the implementation of noise_variance_ in :class:decomposition.PCA.
:issue:9108 by Hanmin Qin <https://github.com/qinhanmin2014>_.
Fixed a bug where :class:cluster.DBSCAN gives incorrect
result when input is a precomputed sparse matrix with initial
rows all zero. :issue:8306 by :user:Akshay Gupta <Akshay0724>
Fix a bug regarding fitting :class:cluster.KMeans with a sparse
array X and initial centroids, where X's means were unnecessarily being
subtracted from the centroids. :issue:7872 by :user:Josh Karnofsky <jkarno>.
Fixes to the input validation in :class:covariance.EllipticEnvelope.
:issue:8086 by Andreas Müller_.
Fixed a bug in :class:covariance.MinCovDet where inputting data
that produced a singular covariance matrix would cause the helper method
_c_step to throw an exception.
:issue:3367 by :user:Jeremy Steward <ThatGeoGuy>
Fixed a bug in :class:manifold.TSNE affecting convergence of the
gradient descent. :issue:8768 by :user:David DeTomaso <deto>.
Fixed a bug in :class:manifold.TSNE where it stored the incorrect
kl_divergence_. :issue:6507 by :user:Sebastian Saeger <ssaeger>.
Fixed improper scaling in :class:cross_decomposition.PLSRegression
with scale=True. :issue:7819 by :user:jayzed82 <jayzed82>.
:class:cluster.SpectralCoclustering and
:class:cluster.SpectralBiclustering fit method conforms
with API by accepting y and returning the object. :issue:6126,
:issue:7814 by :user:Laurent Direr <ldirer> and :user:Maniteja Nandana <maniteja123>.
Fix bug where :mod:sklearn.mixture sample methods did not return as many
samples as requested. :issue:7702 by :user:Levi John Wolf <ljwolf>.
Fixed the shrinkage implementation in :class:neighbors.NearestCentroid.
:issue:9219 by Hanmin Qin <https://github.com/qinhanmin2014>_.
Preprocessing and feature selection
For sparse matrices, :func:preprocessing.normalize with return_norm=True
will now raise a NotImplementedError with 'l1' or 'l2' norm and with
norm 'max' the norms returned will be the same as for dense matrices.
:issue:7771 by Ang Lu <https://github.com/luang008>_.
Fix a bug where :class:feature_selection.SelectFdr did not
exactly implement Benjamini-Hochberg procedure. It formerly may have
selected fewer features than it should.
:issue:7490 by :user:Peng Meng <mpjlu>.
Fixed a bug where linear_model.RandomizedLasso and
linear_model.RandomizedLogisticRegression break for
sparse input. :issue:8259 by :user:Aman Dalmia <dalmia>.
Fix a bug where :class:feature_extraction.FeatureHasher
mandatorily applied a sparse random projection to the hashed features,
preventing the use of
:class:feature_extraction.text.HashingVectorizer in a
pipeline with :class:feature_extraction.text.TfidfTransformer.
:issue:7565 by :user:Roman Yurchak <rth>.
Fix a bug where :class:feature_selection.mutual_info_regression did not
correctly use n_neighbors. :issue:8181 by :user:Guillaume Lemaitre <glemaitre>.
Model evaluation and meta-estimators
Fixed a bug where model_selection.BaseSearchCV.inverse_transform
returns self.best_estimator_.transform() instead of
self.best_estimator_.inverse_transform().
:issue:8344 by :user:Akshay Gupta <Akshay0724> and :user:Rasmus Eriksson <MrMjauh>.
Added classes_ attribute to :class:model_selection.GridSearchCV,
:class:model_selection.RandomizedSearchCV, grid_search.GridSearchCV,
and grid_search.RandomizedSearchCV that matches the classes_
attribute of best_estimator_. :issue:7661 and :issue:8295
by :user:Alyssa Batula <abatula>, :user:Dylan Werner-Meier <unautre>,
and :user:Stephen Hoover <stephen-hoover>.
Fixed a bug where :func:model_selection.validation_curve
reused the same estimator for each parameter value.
:issue:7365 by :user:Aleksandr Sandrovskii <Sundrique>.
:func:model_selection.permutation_test_score now works with Pandas
types. :issue:5697 by :user:Stijn Tonk <equialgo>.
Several fixes to input validation in
:class:multiclass.OutputCodeClassifier
:issue:8086 by Andreas Müller_.
:class:multiclass.OneVsOneClassifier's partial_fit now ensures all
classes are provided up-front. :issue:6250 by
:user:Asish Panda <kaichogami>.
Fix :func:multioutput.MultiOutputClassifier.predict_proba to return a
list of 2d arrays, rather than a 3d array. In the case where different
target columns had different numbers of classes, a ValueError would be
raised on trying to stack matrices with different dimensions.
:issue:8093 by :user:Peter Bull <pjbull>.
Cross validation now works with Pandas datatypes that have a
read-only index. :issue:9507 by Loic Esteve_.
Metrics
:func:metrics.average_precision_score no longer linearly
interpolates between operating points, and instead weighs precisions
by the change in recall since the last operating point, as per the
Wikipedia entry <https://en.wikipedia.org/wiki/Average_precision>.
(#7356 <https://github.com/scikit-learn/scikit-learn/pull/7356>). By
:user:Nick Dingwall <ndingwall> and Gael Varoquaux_.
Fix a bug in metrics.classification._check_targets
which would return 'binary' if y_true and y_pred were
both 'binary' but the union of y_true and y_pred was
'multiclass'. :issue:8377 by Loic Esteve_.
Fixed an integer overflow bug in :func:metrics.confusion_matrix and
hence :func:metrics.cohen_kappa_score. :issue:8354, :issue:7929
by Joel Nothman_ and :user:Jon Crall <Erotemic>.
Fixed passing of gamma parameter to the chi2 kernel in
:func:metrics.pairwise.pairwise_kernels :issue:5211 by
:user:Nick Rhinehart <nrhine1>,
:user:Saurabh Bansod <mth4saurabh> and Andreas Müller_.
Miscellaneous
Fixed a bug when :func:datasets.make_classification fails
when generating more than 30 features. :issue:8159 by
:user:Herilalaina Rakotoarison <herilalaina>.
Fixed a bug where :func:datasets.make_moons gives an
incorrect result when n_samples is odd.
:issue:8198 by :user:Josh Levy <levy5674>.
Some fetch_ functions in :mod:sklearn.datasets were ignoring the
download_if_missing keyword. :issue:7944 by :user:Ralf Gommers <rgommers>.
Fix estimators to accept a sample_weight parameter of type
pandas.Series in their fit function. :issue:7825 by
Kathleen Chen_.
Fix a bug in cases where numpy.cumsum may be numerically unstable,
raising an exception if instability is identified. :issue:7376 and
:issue:7331 by Joel Nothman_ and :user:yangarbiter.
Fix a bug where base.BaseEstimator.__getstate__
obstructed pickling customizations of child-classes, when used in a
multiple inheritance context.
:issue:8316 by :user:Holger Peters <HolgerPeters>.
Update Sphinx-Gallery from 0.1.4 to 0.1.7 for resolving links in
documentation build with Sphinx>1.5 :issue:8010, :issue:7986 by
:user:Oscar Najera <Titan-C>
Add data_home parameter to :func:sklearn.datasets.fetch_kddcup99.
:issue:9289 by Loic Esteve_.
Fix dataset loaders using Python 3 version of makedirs to also work in
Python 2. :issue:9284 by :user:Sebastin Santy <SebastinSanty>.
Several minor issues were fixed with thanks to the alerts of
lgtm.com <https://lgtm.com/>_. :issue:9278 by :user:Jean Helie <jhelie>,
among others.
Trees and ensembles
Gradient boosting base models are no longer estimators. By Andreas Müller_.
All tree-based estimators now accept a min_impurity_decrease
parameter in lieu of the min_impurity_split, which is now deprecated.
The min_impurity_decrease helps stop splitting the nodes in which
the weighted impurity decrease from splitting is no longer at least
min_impurity_decrease. :issue:8449 by Raghav RV_.
Linear, kernelized and related models
n_iter parameter is deprecated in :class:linear_model.SGDClassifier,
:class:linear_model.SGDRegressor,
:class:linear_model.PassiveAggressiveClassifier,
:class:linear_model.PassiveAggressiveRegressor and
:class:linear_model.Perceptron. By Tom Dupre la Tour_.Other predictors
neighbors.LSHForest has been deprecated and will be
removed in 0.21 due to poor performance.
:issue:9078 by :user:Laurent Direr <ldirer>.
:class:neighbors.NearestCentroid no longer purports to support
metric='precomputed' which now raises an error. :issue:8515 by
:user:Sergul Aydore <sergulaydore>.
The alpha parameter of :class:semi_supervised.LabelPropagation now
has no effect and is deprecated to be removed in 0.21. :issue:9239
by :user:Andre Ambrosio Boechat <boechat107>, :user:Utkarsh Upadhyay <musically-ut>, and Joel Nothman_.
Decomposition, manifold learning and clustering
Deprecate the doc_topic_distr argument of the perplexity method
in :class:decomposition.LatentDirichletAllocation because the
user no longer has access to the unnormalized document topic distribution
needed for the perplexity calculation. :issue:7954 by
:user:Gary Foreman <garyForeman>.
The n_topics parameter of :class:decomposition.LatentDirichletAllocation
has been renamed to n_components and will be removed in version 0.21.
:issue:8922 by :user:Attractadore.
:meth:decomposition.SparsePCA.transform's ridge_alpha parameter is
deprecated in preference for class parameter.
:issue:8137 by :user:Naoya Kanai <naoyak>.
:class:cluster.DBSCAN now has a metric_params parameter.
:issue:8139 by :user:Naoya Kanai <naoyak>.
Preprocessing and feature selection
:class:feature_selection.SelectFromModel now has a partial_fit
method only if the underlying estimator does. By Andreas Müller_.
:class:feature_selection.SelectFromModel now validates the threshold
parameter and sets the threshold_ attribute during the call to
fit, and no longer during the call to transform. By Andreas Müller_.
The non_negative parameter in :class:feature_extraction.FeatureHasher
has been deprecated, and replaced with a more principled alternative,
alternate_sign.
:issue:7565 by :user:Roman Yurchak <rth>.
linear_model.RandomizedLogisticRegression,
and linear_model.RandomizedLasso have been deprecated and will
be removed in version 0.21.
:issue:8995 by :user:Ramana.S <sentient07>.
Model evaluation and meta-estimators
Deprecate the fit_params constructor input to the
:class:model_selection.GridSearchCV and
:class:model_selection.RandomizedSearchCV in favor
of passing keyword parameters to the fit methods
of those classes. Data-dependent parameters needed for model
training should be passed as keyword arguments to fit,
and conforming to this convention will allow the hyperparameter
selection classes to be used with tools such as
:func:model_selection.cross_val_predict.
:issue:2879 by :user:Stephen Hoover <stephen-hoover>.
In version 0.21, the default behavior of splitters that use the
test_size and train_size parameter will change, such that
specifying train_size alone will cause test_size to be the
remainder. :issue:7459 by :user:Nelson Liu <nelson-liu>.
:class:multiclass.OneVsRestClassifier now has partial_fit,
decision_function and predict_proba methods only when the
underlying estimator does. :issue:7812 by Andreas Müller_ and
:user:Mikhail Korobov <kmike>.
:class:multiclass.OneVsRestClassifier now has a partial_fit method
only if the underlying estimator does. By Andreas Müller_.
The decision_function output shape for binary classification in
:class:multiclass.OneVsRestClassifier and
:class:multiclass.OneVsOneClassifier is now (n_samples,) to conform
to scikit-learn conventions. :issue:9100 by Andreas Müller_.
The :func:multioutput.MultiOutputClassifier.predict_proba
function used to return a 3d array (n_samples, n_classes,
n_outputs). In the case where different target columns had different
numbers of classes, a ValueError would be raised on trying to stack
matrices with different dimensions. This function now returns a list of
arrays where the length of the list is n_outputs, and each array is
(n_samples, n_classes) for that particular output.
:issue:8093 by :user:Peter Bull <pjbull>.
Replace attribute named_steps dict to :class:utils.Bunch
in :class:pipeline.Pipeline to enable tab completion in interactive
environment. In the case conflict value on named_steps and dict
attribute, dict behavior will be prioritized.
:issue:8481 by :user:Herilalaina Rakotoarison <herilalaina>.
Miscellaneous
Deprecate the y parameter in transform and inverse_transform.
The method should not accept y parameter, as it's used at the prediction time.
:issue:8174 by :user:Tahar Zanouda <tzano>, Alexandre Gramfort_
and Raghav RV_.
SciPy >= 0.13.3 and NumPy >= 1.8.2 are now the minimum supported versions
for scikit-learn. The following backported functions in
:mod:sklearn.utils have been removed or deprecated accordingly.
:issue:8854 and :issue:8874 by :user:Naoya Kanai <naoyak>
The store_covariances and covariances_ parameters of
:class:discriminant_analysis.QuadraticDiscriminantAnalysis
have been renamed to store_covariance and covariance_ to be
consistent with the corresponding parameter names of the
:class:discriminant_analysis.LinearDiscriminantAnalysis. They will be
removed in version 0.21. :issue:7998 by :user:Jiacheng <mrbeann>
Removed in 0.19:
utils.fixes.argpartitionutils.fixes.array_equalutils.fixes.astypeutils.fixes.bincountutils.fixes.expitutils.fixes.frombuffer_emptyutils.fixes.in1dutils.fixes.normutils.fixes.rankdatautils.fixes.safe_copyDeprecated in 0.19, to be removed in 0.21:
utils.arpack.eigsutils.arpack.eigshutils.arpack.svdsutils.extmath.fast_dotutils.extmath.logsumexputils.extmath.normutils.extmath.pinvhutils.graph.graph_laplacianutils.random.choiceutils.sparsetools.connected_componentsutils.stats.rankdataEstimators with both methods decision_function and predict_proba
are now required to have a monotonic relation between them. The
method check_decision_proba_consistency has been added in
utils.estimator_checks to check their consistency.
:issue:7578 by :user:Shubham Bhardwaj <shubham0704>
All checks in utils.estimator_checks, in particular
:func:utils.estimator_checks.check_estimator now accept estimator
instances. Most other checks do not accept
estimator classes any more. :issue:9019 by Andreas Müller_.
Ensure that estimators' attributes ending with _ are not set
in the constructor but only in the fit method. Most notably,
ensemble estimators (deriving from ensemble.BaseEnsemble)
now only have self.estimators_ available after fit.
:issue:7464 by Lars Buitinck_ and Loic Esteve_.
Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.18, including:
Joel Nothman, Loic Esteve, Andreas Mueller, Guillaume Lemaitre, Olivier Grisel, Hanmin Qin, Raghav RV, Alexandre Gramfort, themrmax, Aman Dalmia, Gael Varoquaux, Naoya Kanai, Tom Dupré la Tour, Rishikesh, Nelson Liu, Taehoon Lee, Nelle Varoquaux, Aashil, Mikhail Korobov, Sebastin Santy, Joan Massich, Roman Yurchak, RAKOTOARISON Herilalaina, Thierry Guillemot, Alexandre Abadie, Carol Willing, Balakumaran Manoharan, Josh Karnofsky, Vlad Niculae, Utkarsh Upadhyay, Dmitry Petrov, Minghui Liu, Srivatsan, Vincent Pham, Albert Thomas, Jake VanderPlas, Attractadore, JC Liu, alexandercbooth, chkoar, Óscar Nájera, Aarshay Jain, Kyle Gilliam, Ramana Subramanyam, CJ Carey, Clement Joudet, David Robles, He Chen, Joris Van den Bossche, Karan Desai, Katie Luangkote, Leland McInnes, Maniteja Nandana, Michele Lacchia, Sergei Lebedev, Shubham Bhardwaj, akshay0724, omtcyfz, rickiepark, waterponey, Vathsala Achar, jbDelafosse, Ralf Gommers, Ekaterina Krivich, Vivek Kumar, Ishank Gulati, Dave Elliott, ldirer, Reiichiro Nakano, Levi John Wolf, Mathieu Blondel, Sid Kapur, Dougal J. Sutherland, midinas, mikebenfield, Sourav Singh, Aseem Bansal, Ibraim Ganiev, Stephen Hoover, AishwaryaRK, Steven C. Howell, Gary Foreman, Neeraj Gangwar, Tahar, Jon Crall, dokato, Kathy Chen, ferria, Thomas Moreau, Charlie Brummitt, Nicolas Goix, Adam Kleczewski, Sam Shleifer, Nikita Singh, Basil Beirouti, Giorgio Patrini, Manoj Kumar, Rafael Possas, James Bourbeau, James A. Bednar, Janine Harper, Jaye, Jean Helie, Jeremy Steward, Artsiom, John Wei, Jonathan LIgo, Jonathan Rahn, seanpwilliams, Arthur Mensch, Josh Levy, Julian Kuhlmann, Julien Aubert, Jörn Hees, Kai, shivamgargsya, Kat Hempstalk, Kaushik Lakshmikanth, Kennedy, Kenneth Lyons, Kenneth Myers, Kevin Yap, Kirill Bobyrev, Konstantin Podshumok, Arthur Imbert, Lee Murray, toastedcornflakes, Lera, Li Li, Arthur Douillard, Mainak Jas, tobycheese, Manraj Singh, Manvendra Singh, Marc Meketon, MarcoFalke, Matthew Brett, Matthias Gilch, Mehul Ahuja, Melanie Goetz, Meng, Peng, Michael Dezube, Michal Baumgartner, vibrantabhi19, Artem Golubin, Milen Paskov, Antonin Carette, Morikko, MrMjauh, NALEPA Emmanuel, Namiya, Antoine Wendlinger, Narine Kokhlikyan, NarineK, Nate Guerin, Angus Williams, Ang Lu, Nicole Vavrova, Nitish Pandey, Okhlopkov Daniil Olegovich, Andy Craze, Om Prakash, Parminder Singh, Patrick Carlson, Patrick Pei, Paul Ganssle, Paulo Haddad, Paweł Lorek, Peng Yu, Pete Bachant, Peter Bull, Peter Csizsek, Peter Wang, Pieter Arthur de Jong, Ping-Yao, Chang, Preston Parry, Puneet Mathur, Quentin Hibon, Andrew Smith, Andrew Jackson, 1kastner, Rameshwar Bhaskaran, Rebecca Bilbro, Remi Rampin, Andrea Esuli, Rob Hall, Robert Bradshaw, Romain Brault, Aman Pratik, Ruifeng Zheng, Russell Smith, Sachin Agarwal, Sailesh Choyal, Samson Tan, Samuël Weber, Sarah Brown, Sebastian Pölsterl, Sebastian Raschka, Sebastian Saeger, Alyssa Batula, Abhyuday Pratap Singh, Sergey Feldman, Sergul Aydore, Sharan Yalburgi, willduan, Siddharth Gupta, Sri Krishna, Almer, Stijn Tonk, Allen Riddell, Theofilos Papapanagiotou, Alison, Alexis Mignon, Tommy Boucher, Tommy Löfstedt, Toshihiro Kamishima, Tyler Folkman, Tyler Lanigan, Alexander Junge, Varun Shenoy, Victor Poughon, Vilhelm von Ehrenheim, Aleksandr Sandrovskii, Alan Yee, Vlasios Vasileiou, Warut Vijitbenjaronk, Yang Zhang, Yaroslav Halchenko, Yichuan Liu, Yuichi Fujikawa, affanv14, aivision2020, xor, andreh7, brady salz, campustrampus, Agamemnon Krasoulis, ditenberg, elena-sharova, filipj8, fukatani, gedeck, guiniol, guoci, hakaa1, hongkahjun, i-am-xhy, jakirkham, jaroslaw-weber, jayzed82, jeroko, jmontoyam, jonathan.striebel, josephsalmon, jschendel, leereeves, martin-hahn, mathurinm, mehak-sachdeva, mlewis1729, mlliou112, mthorrell, ndingwall, nuffe, yangarbiter, plagree, pldtc325, Breno Freitas, Brett Olsen, Brian A. Alfano, Brian Burns, polmauri, Brandon Carter, Charlton Austin, Chayant T15h, Chinmaya Pancholi, Christian Danielsen, Chung Yen, Chyi-Kwei Yau, pravarmahajan, DOHMATOB Elvis, Daniel LeJeune, Daniel Hnyk, Darius Morawiec, David DeTomaso, David Gasquez, David Haberthür, David Heryanto, David Kirkby, David Nicholson, rashchedrin, Deborah Gertrude Digges, Denis Engemann, Devansh D, Dickson, Bob Baxley, Don86, E. Lynch-Klarup, Ed Rogers, Elizabeth Ferriss, Ellen-Co2, Fabian Egli, Fang-Chieh Chou, Bing Tian Dai, Greg Stupp, Grzegorz Szpak, Bertrand Thirion, Hadrien Bertrand, Harizo Rajaona, zxcvbnius, Henry Lin, Holger Peters, Icyblade Dai, Igor Andriushchenko, Ilya, Isaac Laughlin, Iván Vallés, Aurélien Bellet, JPFrancoia, Jacob Schreiber, Asish Mahapatra