| AArch64 | |
| ppc64le | |
| s390x | |
| x86-64 |
- specfile:
* update numpy, scipy required versions
* added requirement for threadpoolctl
- update to version 0.23.2:
* sklearn.cluster
+ Fix Fixed a bug in cluster.KMeans where rounding errors could
prevent convergence to be declared when tol=0. #17959 by Jérémie
du Boisberranger.
+ Fix Fixed a bug in cluster.KMeans and cluster.MiniBatchKMeans
where the reported inertia was incorrectly weighted by the
sample weights. #17848 by Jérémie du Boisberranger.
+ Fix Fixed a bug in cluster.MeanShift with bin_seeding=True. When
the estimated bandwidth is 0, the behavior is equivalent to
bin_seeding=False. #17742 by Jeremie du Boisberranger.
+ Fix Fixed a bug in cluster.AffinityPropagation, that gives
incorrect clusters when the array dtype is float32. #17995 by
Thomaz Santana and Amanda Dsouza.
* sklearn.decomposition
+ Fix Fixed a bug in
decomposition.MiniBatchDictionaryLearning.partial_fit which
should update the dictionary by iterating only once over a
mini-batch. #17433 by Chiara Marmo.
+ Fix Avoid overflows on Windows in
decomposition.IncrementalPCA.partial_fit for large batch_size
and n_samples values. #17985 by Alan Butler and Amanda Dsouza.
* sklearn.ensemble
+ Fix Fixed bug in ensemble.MultinomialDeviance where the average
of logloss was incorrectly calculated as sum of logloss. #17694
by Markus Rempfler and Tsutomu Kusanagi.
+ Fix Fixes ensemble.StackingClassifier and
ensemble.StackingRegressor compatibility with estimators that do
not define n_features_in_. #17357 by Thomas Fan.
* sklearn.feature_extraction
+ Fix Fixes bug in feature_extraction.text.CountVectorizer where
sample order invariance was broken when max_features was set and
features had the same count. #18016 by Thomas Fan, Roman
Yurchak, and Joel Nothman.
* sklearn.linear_model
+ Fix linear_model.lars_path does not overwrite X when X_copy=True
and Gram='auto'. #17914 by Thomas Fan.
* sklearn.manifold
+ Fix Fixed a bug where metrics.pairwise_distances would raise an
error if metric='seuclidean' and X is not type
np.float64. #15730 by Forrest Koch.
* sklearn.metrics
+ Fix Fixed a bug in metrics.mean_squared_error where the average
of multiple RMSE values was incorrectly calculated as the root
of the average of multiple MSE values. #17309 by Swier Heeres.
* sklearn.pipeline
+ Fix pipeline.FeatureUnion raises a deprecation warning when None
is included in transformer_list. #17360 by Thomas Fan.
* sklearn.utils
+ Fix Fix utils.estimator_checks.check_estimator so that all test
cases support the binary_only estimator tag. #17812 by Bruno
Charron.
- update to version 0.23.1:
* sklearn.cluster
+ Efficiency cluster.KMeans efficiency has been improved for very
small datasets. In particular it cannot spawn idle threads any
more. #17210 and #17235 by Jeremie du Boisberranger.
+ Fix Fixed a bug in cluster.KMeans where the sample weights
provided by the user were modified in place. #17204 by 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. #17205 by Nicolas Hug.
- changes from version 0.23:
* long list of changes, see
https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-0
for details
- changes from version 0.22.2.post1:
* sklearn.impute
+ Efficiency Reduce impute.KNNImputer asymptotic memory usage by
chunking pairwise distance computation. #16397 by Joel Nothman.
* sklearn.metrics
+ Fix Fixed a bug in metrics.plot_roc_curve where the name of the
estimator was passed in the metrics.RocCurveDisplay instead of
the parameter name. It results in a different plot when calling
metrics.RocCurveDisplay.plot for the subsequent times. #16500 by
Guillaume Lemaitre.
+ Fix Fixed a bug in metrics.plot_precision_recall_curve where the
name of the estimator was passed in the
metrics.PrecisionRecallDisplay instead of the parameter name. It
results in a different plot when calling
metrics.PrecisionRecallDisplay.plot for the subsequent
times. #16505 by Guillaume Lemaitre.
* 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
- Add upstream patch CVE-2024-5206.patch (gh#scikit-learn/scikit-learn@70ca21f106b6, bsc#1226185, CVE-2024-5206)
- specfile:
* update numpy, scipy required versions
* added requirement for threadpoolctl
- update to version 0.23.2:
* sklearn.cluster
+ Fix Fixed a bug in cluster.KMeans where rounding errors could
prevent convergence to be declared when tol=0. #17959 by Jérémie
du Boisberranger.
+ Fix Fixed a bug in cluster.KMeans and cluster.MiniBatchKMeans
where the reported inertia was incorrectly weighted by the
sample weights. #17848 by Jérémie du Boisberranger.
+ Fix Fixed a bug in cluster.MeanShift with bin_seeding=True. When
the estimated bandwidth is 0, the behavior is equivalent to
bin_seeding=False. #17742 by Jeremie du Boisberranger.
+ Fix Fixed a bug in cluster.AffinityPropagation, that gives
incorrect clusters when the array dtype is float32. #17995 by
Thomaz Santana and Amanda Dsouza.
* sklearn.decomposition
+ Fix Fixed a bug in
decomposition.MiniBatchDictionaryLearning.partial_fit which
should update the dictionary by iterating only once over a
mini-batch. #17433 by Chiara Marmo.
+ Fix Avoid overflows on Windows in
decomposition.IncrementalPCA.partial_fit for large batch_size
and n_samples values. #17985 by Alan Butler and Amanda Dsouza.
* sklearn.ensemble
+ Fix Fixed bug in ensemble.MultinomialDeviance where the average
of logloss was incorrectly calculated as sum of logloss. #17694
by Markus Rempfler and Tsutomu Kusanagi.
+ Fix Fixes ensemble.StackingClassifier and
ensemble.StackingRegressor compatibility with estimators that do
not define n_features_in_. #17357 by Thomas Fan.
* sklearn.feature_extraction
+ Fix Fixes bug in feature_extraction.text.CountVectorizer where
sample order invariance was broken when max_features was set and
features had the same count. #18016 by Thomas Fan, Roman
Yurchak, and Joel Nothman.
* sklearn.linear_model
+ Fix linear_model.lars_path does not overwrite X when X_copy=True
and Gram='auto'. #17914 by Thomas Fan.
* sklearn.manifold
+ Fix Fixed a bug where metrics.pairwise_distances would raise an
error if metric='seuclidean' and X is not type
np.float64. #15730 by Forrest Koch.
* sklearn.metrics
+ Fix Fixed a bug in metrics.mean_squared_error where the average
of multiple RMSE values was incorrectly calculated as the root
of the average of multiple MSE values. #17309 by Swier Heeres.
* sklearn.pipeline
+ Fix pipeline.FeatureUnion raises a deprecation warning when None
is included in transformer_list. #17360 by Thomas Fan.
* sklearn.utils
+ Fix Fix utils.estimator_checks.check_estimator so that all test
cases support the binary_only estimator tag. #17812 by Bruno
Charron.
- update to version 0.23.1:
* sklearn.cluster
+ Efficiency cluster.KMeans efficiency has been improved for very
small datasets. In particular it cannot spawn idle threads any
more. #17210 and #17235 by Jeremie du Boisberranger.
+ Fix Fixed a bug in cluster.KMeans where the sample weights
provided by the user were modified in place. #17204 by 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. #17205 by Nicolas Hug.
- changes from version 0.23:
* long list of changes, see
https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-0
for details
- changes from version 0.22.2.post1:
* sklearn.impute
+ Efficiency Reduce impute.KNNImputer asymptotic memory usage by
chunking pairwise distance computation. #16397 by Joel Nothman.
* sklearn.metrics
+ Fix Fixed a bug in metrics.plot_roc_curve where the name of the
estimator was passed in the metrics.RocCurveDisplay instead of
the parameter name. It results in a different plot when calling
metrics.RocCurveDisplay.plot for the subsequent times. #16500 by
Guillaume Lemaitre.
+ Fix Fixed a bug in metrics.plot_precision_recall_curve where the
name of the estimator was passed in the
metrics.PrecisionRecallDisplay instead of the parameter name. It
results in a different plot when calling
metrics.PrecisionRecallDisplay.plot for the subsequent
times. #16505 by Guillaume Lemaitre.
* 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
- added provide sklearn and python-sklearn as this is name most users will search for
- Require at least pytest 4.x for testing
- Update to Version 0.21.3
+ 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`.
* Fix: Fixed a bug in :class:`cluster.OPTICS` where users were unable to pass
float `min_samples` and `min_cluster_size`.
* Fix: Fixed a bug in :class:`cluster.KMeans` where KMeans++ initialisation
could rarely result in an IndexError..
+ 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.
+ 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.
.
+ sklearn.ensemble
* Fix: Fix zero division error in :func:`HistGradientBoostingClassifier` and
:func:`HistGradientBoostingRegressor`.
+ 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.
+ sklearn.inspection
* Fix: Fixed a bug in :func:`inspection.plot_partial_dependence` where
``target`` parameter was not being taken into account for multiclass problems.
:pr:`14393` by :user:`Guillem G. Subies <guillemgsubies>`.
+ 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).
* 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.
+ 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.
+ sklearn.tree
* Fix: Fixed bug in :func:`tree.export_text` when the tree has one feature and
a single feature name is passed in.
* Fix: Fixed an issue with :func:`plot_tree` where it displayed
entropy calculations even for `gini` criterion in DecisionTreeClassifiers.
- Update to Version 0.21.2
+ sklearn.decomposition
* Fix: Fixed a bug in cross_decomposition.CCA improving numerical
stability when Y is close to zero..
+ sklearn.metrics
* Fix: Fixed a bug in metrics.euclidean_distances where a part of the
distance matrix was left un-instanciated for suffiently large float32
datasets (regression introduced in 0.21)..
+ sklearn.preprocessing
* Fix: Fixed a bug in preprocessing.OneHotEncoder where the new
drop parameter was not reflected in get_feature_names..
+ sklearn.utils.sparsefuncs
* Fix: Fixed a bug where min_max_axis would fail on 32-bit systems
for certain large inputs. This affects preprocessing.MaxAbsScaler,
preprocessing.normalize and preprocessing.LabelBinarizer..
- Update to Version 0.21.1
+ sklearn.metrics
* Fix: Fixed a bug in metrics.pairwise_distances where it would raise
AttributeError for boolean metrics when X had a boolean dtype and
Y == None..
* Fix: Fixed two bugs in 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..
+ sklearn.neighbors
* Fix: Fixed a bug in neighbors.KernelDensity which could not be
restored from a pickle if sample_weight had been used..
- Update to Version 0.21.0
+ 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.
* discriminant_analysis.LinearDiscriminantAnalysis for multiclass
classification. |Fix|
* discriminant_analysis.LinearDiscriminantAnalysis with 'eigen'
solver. |Fix|
* linear_model.BayesianRidge |Fix|
* Decision trees and derived ensembles when both max_depth and
max_leaf_nodes are set. |Fix|
* linear_model.LogisticRegression and
linear_model.LogisticRegressionCV with 'saga' solver. |Fix|
* ensemble.GradientBoostingClassifier |Fix|
* sklearn.feature_extraction.text.HashingVectorizer,
sklearn.feature_extraction.text.TfidfVectorizer, and
sklearn.feature_extraction.text.CountVectorizer |Fix|
* neural_network.MLPClassifier |Fix|
* svm.SVC.decision_function and
multiclass.OneVsOneClassifier.decision_function. |Fix|
* linear_model.SGDClassifier and any derived classifiers. |Fix|
* Any model using the linear_model.sag.sag_solver function with a 0
seed, including linear_model.LogisticRegression,
linear_model.LogisticRegressionCV, linear_model.Ridge,
and linear_model.RidgeCV with 'sag' solver. |Fix|
* linear_model.RidgeCV when using generalized 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 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 released in version 0.16.
In a future release we hope to choose better default max_iter and tol
heuristically depending on the solver.
+ Support for Python 3.4 and below has been officially dropped.
+ 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
metrics.r2_score. This will influence the score method of all
the multioutput regressors (except for
multioutput.MultiOutputRegressor)..
+ sklearn.calibration
* Enhancement: Added support to bin the data passed into
calibration.calibration_curve by quantiles instead of uniformly
between 0 and 1..
* Enhancement: Allow n-dimensional arrays as input for
calibration.CalibratedClassifierCV..
+ sklearn.cluster
* MajorFeature: A new clustering algorithm: cluster.OPTICS: an
algoritm related to cluster.DBSCAN, that has hyperparameters easier
to set and that scales better,
* Fix: Fixed a bug where cluster.Birch could occasionally raise an
AttributeError..
* Fix: Fixed a bug in cluster.KMeans where empty clusters weren't
correctly relocated when using sample weights..
* API: The n_components_ attribute in cluster.AgglomerativeClustering
and cluster.FeatureAgglomeration has been renamed to
n_connected_components_..
* Enhancement: cluster.AgglomerativeClustering and
cluster.FeatureAgglomeration now accept a distance_threshold
parameter which can be used to find the clusters instead of n_clusters.
+ sklearn.compose
* API: compose.ColumnTransformer is no longer an experimental
feature..
+ sklearn.datasets
* Fix: Added support for 64-bit group IDs and pointers in SVMLight files..
* Fix: datasets.load_sample_images returns images with a deterministic
order..
+ sklearn.decomposition
* Enhancement: decomposition.KernelPCA now has deterministic output
(resolved sign ambiguity in eigenvalue decomposition of the kernel matrix)..
* Fix: Fixed a bug in 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.
* Fix: Fixed a bug in 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).
* API: The default value of the init argument in
decomposition.non_negative_factorization will change from
random to None in version 0.23 to make it consistent with
decomposition.NMF. A FutureWarning is raised when
the default value is used..
+ sklearn.discriminant_analysis
* Enhancement: discriminant_analysis.LinearDiscriminantAnalysis now
preserves float32 and float64 dtypes.
* Fix: A ChangedBehaviourWarning is now raised when
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..
* Fix: Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis
where the predicted probabilities would be incorrectly computed in the
multiclass case.
* Fix: Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis
where the predicted probabilities would be incorrectly computed with eigen
solver.
+ sklearn.dummy
* Fix: Fixed a bug in dummy.DummyClassifier where the
predict_proba method was returning int32 array instead of
float64 for the stratified strategy..
* Fix: Fixed a bug in 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.
+ sklearn.ensemble
* MajorFeature: Add two new implementations of
gradient boosting trees: ensemble.HistGradientBoostingClassifier
and ensemble.HistGradientBoostingRegressor. The implementation of
these estimators is inspired by
LightGBM and can be orders of
magnitude faster than ensemble.GradientBoostingRegressor and
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
ensemble.GradientBoostingClassifier and
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.
* Feature: Add ensemble.VotingRegressor
which provides an equivalent of ensemble.VotingClassifier
for regression problems.
* Efficiency: Make ensemble.IsolationForest prefer threads over
processes when running with n_jobs > 1 as the underlying decision tree
fit calls do release the GIL. This changes reduces memory usage and
communication overhead.
* Efficiency: Make ensemble.IsolationForest more memory efficient
by avoiding keeping in memory each tree prediction..
* Efficiency: ensemble.IsolationForest now uses chunks of data at
prediction step, thus capping the memory usage..
* Efficiency: sklearn.ensemble.GradientBoostingClassifier and
sklearn.ensemble.GradientBoostingRegressor now keep the
input y as float64 to avoid it being copied internally by trees..
* Enhancement: Minimized the validation of X in
ensemble.AdaBoostClassifier and ensemble.AdaBoostRegressor.
* Enhancement: ensemble.IsolationForest now exposes warm_start
parameter, allowing iterative addition of trees to an isolation
forest..
* Fix: The values of feature_importances_ in all random forest based
models (i.e.
ensemble.RandomForestClassifier,
ensemble.RandomForestRegressor,
ensemble.ExtraTreesClassifier,
ensemble.ExtraTreesRegressor,
ensemble.RandomTreesEmbedding,
ensemble.GradientBoostingClassifier, and
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.
* Fix: Fixed a bug in ensemble.GradientBoostingClassifier and
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. and.
* Fix: Fixed the output of the average path length computed in
ensemble.IsolationForest when the input is either 0, 1 or 2.
* Fix: Fixed a bug in ensemble.GradientBoostingClassifier where
the gradients would be incorrectly computed in multiclass classification
problems..
* Fix: Fixed a bug in ensemble.GradientBoostingClassifier where
validation sets for early stopping were not sampled with stratification..
* Fix: Fixed a bug in ensemble.GradientBoostingClassifier where
the default initial prediction of a multiclass classifier would predict the
classes priors instead of the log of the priors..
* Fix: Fixed a bug in ensemble.RandomForestClassifier where the
predict method would error for multiclass multioutput forests models
if any targets were strings..
* 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..
* Fix: ensemble.partial_dependence (and consequently the new
version 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..
* API: ensemble.partial_dependence and
ensemble.plot_partial_dependence are now deprecated in favor of
inspection.partial_dependence
and
inspection.plot_partial_dependence. and
* Fix: ensemble.VotingClassifier and
ensemble.VotingRegressor were failing during fit in one
of the estimators was set to None and sample_weight was not None..
* API: ensemble.VotingClassifier and
ensemble.VotingRegressor accept 'drop' to disable an estimator
in addition to None to be consistent with other estimators (i.e.,
pipeline.FeatureUnion and compose.ColumnTransformer)..
+ sklearn.externals
* API: Deprecated externals.six since we have dropped support for
Python 2.7..
+ sklearn.feature_extraction
* Fix: If input='file' or input='filename', and a callable is given as
the analyzer, sklearn.feature_extraction.text.HashingVectorizer,
sklearn.feature_extraction.text.TfidfVectorizer, and
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..
+ sklearn.impute
* MajorFeature: Added 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.
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 impute.SimpleImputer and
impute.IterativeImputer have a new parameter 'add_indicator',
which simply stacks a impute.MissingIndicator transform into the
output of the imputer's transform. That allows a predictive estimator to
account for missingness.
* Fix: In impute.MissingIndicator avoid implicit densification by
raising an exception if input is sparse add missing_values property
is set to 0..
* Fix: Fixed two bugs in 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..
+ 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).
+ sklearn.isotonic
* Feature: Allow different dtypes (such as float32) in
isotonic.IsotonicRegression.
+ sklearn.linear_model
* Enhancement: linear_model.Ridge now preserves float32 and
float64 dtypes.
* Feature: linear_model.LogisticRegression and
linear_model.LogisticRegressionCV now support Elastic-Net penalty,
with the 'saga' solver..
* Feature: Added linear_model.lars_path_gram, which is
linear_model.lars_path in the sufficient stats mode, allowing
users to compute linear_model.lars_path without providing
X and y..
* Efficiency: linear_model.make_dataset now preserves
float32 and float64 dtypes, reducing memory consumption in stochastic
gradient, SAG and SAGA solvers.
* Enhancement: 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..
* Enhancement: sparse_cg solver in linear_model.Ridge
now supports fitting the intercept (i.e. fit_intercept=True) when
inputs are sparse..
* Enhancement: The coordinate descent solver used in Lasso, ElasticNet,
etc. now issues a ConvergenceWarning when it completes without meeting the
desired toleranbce.
* Fix: Fixed a bug in linear_model.LogisticRegression and
linear_model.LogisticRegressionCV with 'saga' solver, where the
weights would not be correctly updated in some cases..
* Fix: Fixed the posterior mean, posterior covariance and returned
regularization parameters in 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..
* Fix: Fixed a bug in linear_model.LassoLarsIC, where user input
copy_X=False at instance creation would be overridden by default
parameter value copy_X=True in fit.
* Fix: Fixed a bug in linear_model.LinearRegression that
was not returning the same coeffecients and intercepts with
fit_intercept=True in sparse and dense case.
* Fix: Fixed a bug in linear_model.HuberRegressor that was
broken when X was of dtype bool..
* Fix: Fixed a performance issue of saga and sag solvers when called
in a joblib.Parallel setting with n_jobs > 1 and
backend="threading", causing them to perform worse than in the sequential
case..
* Fix: Fixed a bug in
linear_model.stochastic_gradient.BaseSGDClassifier that was not
deterministic when trained in a multi-class setting on several threads..
* Fix: Fixed bug in linear_model.ridge_regression,
linear_model.Ridge and
linear_model.RidgeClassifier that
caused unhandled exception for arguments return_intercept=True and
solver=auto (default) or any other solver different from sag.
* Fix: linear_model.ridge_regression will now raise an exception
if return_intercept=True and solver is different from sag. Previously,
only warning was issued.
* Fix: 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).
* API: The use of 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 linear_model.lars_path_gram instead..
* API: linear_model.logistic_regression_path is deprecated
in version 0.21 and will be removed in version 0.23..
* Fix: linear_model.RidgeCV with generalized cross-validation
now correctly fits an intercept when fit_intercept=True and the design
matrix is sparse.
+ sklearn.manifold
* Efficiency: Make manifold.tsne.trustworthiness use an inverted index
instead of an np.where lookup to find the rank of neighbors in the input
space. This improves efficiency in particular when computed with
lots of neighbors and/or small datasets..
+ sklearn.metrics
* Feature: Added the metrics.max_error metric and a corresponding
'max_error' scorer for single output regression..
* Feature: Add 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.
* Feature: 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 metrics.f1_score.
* Feature: Added metrics.pairwise.haversine_distances which can be
accessed with metric='pairwise' through metrics.pairwise_distances
and estimators. (Haversine distance was previously available for nearest
neighbors calculation.)
* Efficiency: Faster metrics.pairwise_distances with n_jobs
> 1 by using a thread-based backend, instead of process-based backends.
* Efficiency: The pairwise manhattan distances with sparse input now uses the
BLAS shipped with scipy instead of the bundled BLAS.
* Enhancement: Use label accuracy instead of micro-average on
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.
* Enhancement: Added beta parameter to
metrics.homogeneity_completeness_v_measure and
metrics.v_measure_score to configure the
tradeoff between homogeneity and completeness.
* Fix: The metric metrics.r2_score is degenerate with a single sample
and now it returns NaN and raises exceptions.UndefinedMetricWarning..
* Fix: Fixed a bug where metrics.brier_score_loss will sometimes
return incorrect result when there's only one class in y_true..
* Fix: Fixed a bug in metrics.label_ranking_average_precision_score
where sample_weight wasn't taken into account for samples with degenerate
labels..
* API: The parameter labels in metrics.hamming_loss is deprecated
in version 0.21 and will be removed in version 0.23.
* Fix: The function 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.
* API: metrics.jaccard_similarity_score is deprecated in favour of
the more consistent metrics.jaccard_score. The former behavior for
binary and multiclass targets is broken..
+ sklearn.mixture
* Fix: Fixed a bug in mixture.BaseMixture and therefore on estimators
based on it, i.e. mixture.GaussianMixture and
mixture.BayesianGaussianMixture, where fit_predict and
fit.predict were not equivalent..
+ sklearn.model_selection
* Feature: Classes ~model_selection.GridSearchCV and
~model_selection.RandomizedSearchCV now allow for refit=callable
to add flexibility in identifying the best estimator.
See sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py.
* Enhancement: Classes ~model_selection.GridSearchCV,
~model_selection.RandomizedSearchCV, and methods
~model_selection.cross_val_score,
~model_selection.cross_val_predict,
~model_selection.cross_validate, now print train scores when
return_train_scores is True and verbose > 2. For
~model_selection.learning_curve, and
~model_selection.validation_curve only the latter is required.
* Enhancement: Some CV splitter classes and
model_selection.train_test_split now raise ValueError when the
resulting training set is empty..
* Fix: Fixed a bug where model_selection.StratifiedKFold
shuffles each class's samples with the same random_state,
making shuffle=True ineffective..
* Fix: Added ability for model_selection.cross_val_predict to handle
multi-label (and multioutput-multiclass) targets with predict_proba-type
methods..
* Fix: Fixed an issue in ~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.
+ sklearn.multiclass
* Fix: Fixed an issue in 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..
+ sklearn.multioutput
* Fix: Fixed a bug in multioutput.MultiOutputClassifier where the
predict_proba method incorrectly checked for predict_proba attribute in
the estimator object.
+ sklearn.neighbors
* MajorFeature: Added neighbors.NeighborhoodComponentsAnalysis for
metric learning, which implements the Neighborhood Components Analysis
algorithm.
* API: Methods in neighbors.NearestNeighbors :
~neighbors.NearestNeighbors.kneighbors,
~neighbors.NearestNeighbors.radius_neighbors,
~neighbors.NearestNeighbors.kneighbors_graph,
~neighbors.NearestNeighbors.radius_neighbors_graph
now raise NotFittedError, rather than AttributeError,
when called before fit.
+ sklearn.neural_network
* Fix: Fixed a bug in neural_network.MLPClassifier and
neural_network.MLPRegressor where the option shuffle=False
was being ignored..
* Fix: Fixed a bug in neural_network.MLPClassifier where
validation sets for early stopping were not sampled with stratification. In
the multilabel case however, splits are still not stratified..
+ sklearn.pipeline
* Feature: 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..
* Feature: Added optional parameter verbose in pipeline.Pipeline,
compose.ColumnTransformer and pipeline.FeatureUnion
and corresponding make_ helpers for showing progress and timing of
each step.
* Enhancement: pipeline.Pipeline now supports using 'passthrough'
as a transformer, with the same effect as None..
* Enhancement: pipeline.Pipeline implements __len__ and
therefore len(pipeline) returns the number of steps in the pipeline..
+ sklearn.preprocessing
* Feature: preprocessing.OneHotEncoder now supports dropping one
feature per category with a new drop parameter..
* Efficiency: preprocessing.OneHotEncoder and
preprocessing.OrdinalEncoder now handle pandas DataFrames more
efficiently..
* Efficiency: Make preprocessing.MultiLabelBinarizer cache class
mappings instead of calculating it every time on the fly.
* Efficiency: 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..
* Efficiency: Speed improvement in preprocessing.PolynomialFeatures,
in the dense case. Also added a new parameter order which controls output
order for further speed performances..
* Fix: Fixed the calculation overflow when using a float16 dtype with
preprocessing.StandardScaler.
* Fix: Fixed a bug in preprocessing.QuantileTransformer and
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..
* API: The default value of copy in 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
preprocessing and prevent unexpected side effects by modifying
the value of X inplace..
+ sklearn.svm
* Fix: Fixed an issue in 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..
+ 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..
* Feature: Decision Trees can now be exported in a human readable
textual format using tree.export_text.
* Feature: get_n_leaves() and get_depth() have been added to
tree.BaseDecisionTree and consequently all estimators based
on it, including tree.DecisionTreeClassifier,
tree.DecisionTreeRegressor, tree.ExtraTreeClassifier,
and tree.ExtraTreeRegressor..
* Fix: Trees and forests did not previously predict multi-output
classification targets with string labels, despite accepting them in fit..
* Fix: Fixed an issue with tree.BaseDecisionTree
and consequently all estimators based
on it, including tree.DecisionTreeClassifier,
tree.DecisionTreeRegressor, tree.ExtraTreeClassifier,
and 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..
+ sklearn.utils
* Feature: utils.resample now accepts a stratify parameter for
sampling according to class distributions..
* API: Deprecated warn_on_dtype parameter from utils.check_array
and utils.check_X_y. Added explicit warning for dtype conversion
in check_pairwise_arrays if the metric being passed is a
pairwise boolean metric..
+ 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
sklearn.set_config..
* 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.
* Efficiency: Memory copies are avoided when casting arrays to a different
dtype in multiple estimators..
* Fix: Fixed a bug in the implementation of the our_rand_r
helper function that was not behaving consistently across platforms.
+ 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..
+ Changes to estimator checks
These changes mostly affect library developers.
* Add check_fit_idempotent to
~utils.estimator_checks.check_estimator, which checks that
when fit is called twice with the same data, the ouput of
predict, predict_proba, transform, and decision_function does not
change.
* Many checks can now be disabled or configured with estimator_tags..
- Switch off tests, gh#scikit-learn/scikit-learn#12369
- update to 0.20.0: * http://scikit-learn.org/stable/whats_new.html#version-0-20-0 Support for Python 3.3 has been officially dropped - drop scikit-learn-skip-test.patch (merged)
- Update to version 0.17
- added provide sklearn and python-sklearn as this is name most users will search for
- Require at least pytest 4.x for testing
- version update to 0.22.1 * Clear definition of the public API * Deprecations: using FutureWarning from now on * Certain estimators and functions may produce different models * See https://scikit-learn.org/stable/whats_new/v0.22.html#version-0-22-0
- Update to Version 0.21.3
+ 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`.
* Fix: Fixed a bug in :class:`cluster.OPTICS` where users were unable to pass
float `min_samples` and `min_cluster_size`.
* Fix: Fixed a bug in :class:`cluster.KMeans` where KMeans++ initialisation
could rarely result in an IndexError..
+ 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.
+ 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.
.
+ sklearn.ensemble
* Fix: Fix zero division error in :func:`HistGradientBoostingClassifier` and
:func:`HistGradientBoostingRegressor`.
+ 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.
+ sklearn.inspection
* Fix: Fixed a bug in :func:`inspection.plot_partial_dependence` where
``target`` parameter was not being taken into account for multiclass problems.
:pr:`14393` by :user:`Guillem G. Subies <guillemgsubies>`.
+ 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).
* 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.
+ 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.
+ sklearn.tree
* Fix: Fixed bug in :func:`tree.export_text` when the tree has one feature and
a single feature name is passed in.
* Fix: Fixed an issue with :func:`plot_tree` where it displayed
entropy calculations even for `gini` criterion in DecisionTreeClassifiers.
- Update to Version 0.21.2
+ sklearn.decomposition
* Fix: Fixed a bug in cross_decomposition.CCA improving numerical
stability when Y is close to zero..
+ sklearn.metrics
* Fix: Fixed a bug in metrics.euclidean_distances where a part of the
distance matrix was left un-instanciated for suffiently large float32
datasets (regression introduced in 0.21)..
+ sklearn.preprocessing
* Fix: Fixed a bug in preprocessing.OneHotEncoder where the new
drop parameter was not reflected in get_feature_names..
+ sklearn.utils.sparsefuncs
* Fix: Fixed a bug where min_max_axis would fail on 32-bit systems
for certain large inputs. This affects preprocessing.MaxAbsScaler,
preprocessing.normalize and preprocessing.LabelBinarizer..
- Update to Version 0.21.1
+ sklearn.metrics
* Fix: Fixed a bug in metrics.pairwise_distances where it would raise
AttributeError for boolean metrics when X had a boolean dtype and
Y == None..
* Fix: Fixed two bugs in 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..
+ sklearn.neighbors
* Fix: Fixed a bug in neighbors.KernelDensity which could not be
restored from a pickle if sample_weight had been used..
- Update to Version 0.21.0
+ 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.
* discriminant_analysis.LinearDiscriminantAnalysis for multiclass
classification. |Fix|
* discriminant_analysis.LinearDiscriminantAnalysis with 'eigen'
solver. |Fix|
* linear_model.BayesianRidge |Fix|
* Decision trees and derived ensembles when both max_depth and
max_leaf_nodes are set. |Fix|
* linear_model.LogisticRegression and
linear_model.LogisticRegressionCV with 'saga' solver. |Fix|
* ensemble.GradientBoostingClassifier |Fix|
* sklearn.feature_extraction.text.HashingVectorizer,
sklearn.feature_extraction.text.TfidfVectorizer, and
sklearn.feature_extraction.text.CountVectorizer |Fix|
* neural_network.MLPClassifier |Fix|
* svm.SVC.decision_function and
multiclass.OneVsOneClassifier.decision_function. |Fix|
* linear_model.SGDClassifier and any derived classifiers. |Fix|
* Any model using the linear_model.sag.sag_solver function with a 0
seed, including linear_model.LogisticRegression,
linear_model.LogisticRegressionCV, linear_model.Ridge,
and linear_model.RidgeCV with 'sag' solver. |Fix|
* linear_model.RidgeCV when using generalized 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 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 released in version 0.16.
In a future release we hope to choose better default max_iter and tol
heuristically depending on the solver.
+ Support for Python 3.4 and below has been officially dropped.
+ 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
metrics.r2_score. This will influence the score method of all
the multioutput regressors (except for
multioutput.MultiOutputRegressor)..
+ sklearn.calibration
* Enhancement: Added support to bin the data passed into
calibration.calibration_curve by quantiles instead of uniformly
between 0 and 1..
* Enhancement: Allow n-dimensional arrays as input for
calibration.CalibratedClassifierCV..
+ sklearn.cluster
* MajorFeature: A new clustering algorithm: cluster.OPTICS: an
algoritm related to cluster.DBSCAN, that has hyperparameters easier
to set and that scales better,
* Fix: Fixed a bug where cluster.Birch could occasionally raise an
AttributeError..
* Fix: Fixed a bug in cluster.KMeans where empty clusters weren't
correctly relocated when using sample weights..
* API: The n_components_ attribute in cluster.AgglomerativeClustering
and cluster.FeatureAgglomeration has been renamed to
n_connected_components_..
* Enhancement: cluster.AgglomerativeClustering and
cluster.FeatureAgglomeration now accept a distance_threshold
parameter which can be used to find the clusters instead of n_clusters.
+ sklearn.compose
* API: compose.ColumnTransformer is no longer an experimental
feature..
+ sklearn.datasets
* Fix: Added support for 64-bit group IDs and pointers in SVMLight files..
* Fix: datasets.load_sample_images returns images with a deterministic
order..
+ sklearn.decomposition
* Enhancement: decomposition.KernelPCA now has deterministic output
(resolved sign ambiguity in eigenvalue decomposition of the kernel matrix)..
* Fix: Fixed a bug in 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.
* Fix: Fixed a bug in 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).
* API: The default value of the init argument in
decomposition.non_negative_factorization will change from
random to None in version 0.23 to make it consistent with
decomposition.NMF. A FutureWarning is raised when
the default value is used..
+ sklearn.discriminant_analysis
* Enhancement: discriminant_analysis.LinearDiscriminantAnalysis now
preserves float32 and float64 dtypes.
* Fix: A ChangedBehaviourWarning is now raised when
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..
* Fix: Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis
where the predicted probabilities would be incorrectly computed in the
multiclass case.
* Fix: Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis
where the predicted probabilities would be incorrectly computed with eigen
solver.
+ sklearn.dummy
* Fix: Fixed a bug in dummy.DummyClassifier where the
predict_proba method was returning int32 array instead of
float64 for the stratified strategy..
* Fix: Fixed a bug in 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.
+ sklearn.ensemble
* MajorFeature: Add two new implementations of
gradient boosting trees: ensemble.HistGradientBoostingClassifier
and ensemble.HistGradientBoostingRegressor. The implementation of
these estimators is inspired by
LightGBM and can be orders of
magnitude faster than ensemble.GradientBoostingRegressor and
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
ensemble.GradientBoostingClassifier and
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.
* Feature: Add ensemble.VotingRegressor
which provides an equivalent of ensemble.VotingClassifier
for regression problems.
* Efficiency: Make ensemble.IsolationForest prefer threads over
processes when running with n_jobs > 1 as the underlying decision tree
fit calls do release the GIL. This changes reduces memory usage and
communication overhead.
* Efficiency: Make ensemble.IsolationForest more memory efficient
by avoiding keeping in memory each tree prediction..
* Efficiency: ensemble.IsolationForest now uses chunks of data at
prediction step, thus capping the memory usage..
* Efficiency: sklearn.ensemble.GradientBoostingClassifier and
sklearn.ensemble.GradientBoostingRegressor now keep the
input y as float64 to avoid it being copied internally by trees..
* Enhancement: Minimized the validation of X in
ensemble.AdaBoostClassifier and ensemble.AdaBoostRegressor.
* Enhancement: ensemble.IsolationForest now exposes warm_start
parameter, allowing iterative addition of trees to an isolation
forest..
* Fix: The values of feature_importances_ in all random forest based
models (i.e.
ensemble.RandomForestClassifier,
ensemble.RandomForestRegressor,
ensemble.ExtraTreesClassifier,
ensemble.ExtraTreesRegressor,
ensemble.RandomTreesEmbedding,
ensemble.GradientBoostingClassifier, and
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.
* Fix: Fixed a bug in ensemble.GradientBoostingClassifier and
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. and.
* Fix: Fixed the output of the average path length computed in
ensemble.IsolationForest when the input is either 0, 1 or 2.
* Fix: Fixed a bug in ensemble.GradientBoostingClassifier where
the gradients would be incorrectly computed in multiclass classification
problems..
* Fix: Fixed a bug in ensemble.GradientBoostingClassifier where
validation sets for early stopping were not sampled with stratification..
* Fix: Fixed a bug in ensemble.GradientBoostingClassifier where
the default initial prediction of a multiclass classifier would predict the
classes priors instead of the log of the priors..
* Fix: Fixed a bug in ensemble.RandomForestClassifier where the
predict method would error for multiclass multioutput forests models
if any targets were strings..
* 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..
* Fix: ensemble.partial_dependence (and consequently the new
version 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..
* API: ensemble.partial_dependence and
ensemble.plot_partial_dependence are now deprecated in favor of
inspection.partial_dependence
and
inspection.plot_partial_dependence. and
* Fix: ensemble.VotingClassifier and
ensemble.VotingRegressor were failing during fit in one
of the estimators was set to None and sample_weight was not None..
* API: ensemble.VotingClassifier and
ensemble.VotingRegressor accept 'drop' to disable an estimator
in addition to None to be consistent with other estimators (i.e.,
pipeline.FeatureUnion and compose.ColumnTransformer)..
+ sklearn.externals
* API: Deprecated externals.six since we have dropped support for
Python 2.7..
+ sklearn.feature_extraction
* Fix: If input='file' or input='filename', and a callable is given as
the analyzer, sklearn.feature_extraction.text.HashingVectorizer,
sklearn.feature_extraction.text.TfidfVectorizer, and
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..
+ sklearn.impute
* MajorFeature: Added 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.
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 impute.SimpleImputer and
impute.IterativeImputer have a new parameter 'add_indicator',
which simply stacks a impute.MissingIndicator transform into the
output of the imputer's transform. That allows a predictive estimator to
account for missingness.
* Fix: In impute.MissingIndicator avoid implicit densification by
raising an exception if input is sparse add missing_values property
is set to 0..
* Fix: Fixed two bugs in 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..
+ 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).
+ sklearn.isotonic
* Feature: Allow different dtypes (such as float32) in
isotonic.IsotonicRegression.
+ sklearn.linear_model
* Enhancement: linear_model.Ridge now preserves float32 and
float64 dtypes.
* Feature: linear_model.LogisticRegression and
linear_model.LogisticRegressionCV now support Elastic-Net penalty,
with the 'saga' solver..
* Feature: Added linear_model.lars_path_gram, which is
linear_model.lars_path in the sufficient stats mode, allowing
users to compute linear_model.lars_path without providing
X and y..
* Efficiency: linear_model.make_dataset now preserves
float32 and float64 dtypes, reducing memory consumption in stochastic
gradient, SAG and SAGA solvers.
* Enhancement: 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..
* Enhancement: sparse_cg solver in linear_model.Ridge
now supports fitting the intercept (i.e. fit_intercept=True) when
inputs are sparse..
* Enhancement: The coordinate descent solver used in Lasso, ElasticNet,
etc. now issues a ConvergenceWarning when it completes without meeting the
desired toleranbce.
* Fix: Fixed a bug in linear_model.LogisticRegression and
linear_model.LogisticRegressionCV with 'saga' solver, where the
weights would not be correctly updated in some cases..
* Fix: Fixed the posterior mean, posterior covariance and returned
regularization parameters in 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..
* Fix: Fixed a bug in linear_model.LassoLarsIC, where user input
copy_X=False at instance creation would be overridden by default
parameter value copy_X=True in fit.
* Fix: Fixed a bug in linear_model.LinearRegression that
was not returning the same coeffecients and intercepts with
fit_intercept=True in sparse and dense case.
* Fix: Fixed a bug in linear_model.HuberRegressor that was
broken when X was of dtype bool..
* Fix: Fixed a performance issue of saga and sag solvers when called
in a joblib.Parallel setting with n_jobs > 1 and
backend="threading", causing them to perform worse than in the sequential
case..
* Fix: Fixed a bug in
linear_model.stochastic_gradient.BaseSGDClassifier that was not
deterministic when trained in a multi-class setting on several threads..
* Fix: Fixed bug in linear_model.ridge_regression,
linear_model.Ridge and
linear_model.RidgeClassifier that
caused unhandled exception for arguments return_intercept=True and
solver=auto (default) or any other solver different from sag.
* Fix: linear_model.ridge_regression will now raise an exception
if return_intercept=True and solver is different from sag. Previously,
only warning was issued.
* Fix: 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).
* API: The use of 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 linear_model.lars_path_gram instead..
* API: linear_model.logistic_regression_path is deprecated
in version 0.21 and will be removed in version 0.23..
* Fix: linear_model.RidgeCV with generalized cross-validation
now correctly fits an intercept when fit_intercept=True and the design
matrix is sparse.
+ sklearn.manifold
* Efficiency: Make manifold.tsne.trustworthiness use an inverted index
instead of an np.where lookup to find the rank of neighbors in the input
space. This improves efficiency in particular when computed with
lots of neighbors and/or small datasets..
+ sklearn.metrics
* Feature: Added the metrics.max_error metric and a corresponding
'max_error' scorer for single output regression..
* Feature: Add 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.
* Feature: 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 metrics.f1_score.
* Feature: Added metrics.pairwise.haversine_distances which can be
accessed with metric='pairwise' through metrics.pairwise_distances
and estimators. (Haversine distance was previously available for nearest
neighbors calculation.)
* Efficiency: Faster metrics.pairwise_distances with n_jobs
> 1 by using a thread-based backend, instead of process-based backends.
* Efficiency: The pairwise manhattan distances with sparse input now uses the
BLAS shipped with scipy instead of the bundled BLAS.
* Enhancement: Use label accuracy instead of micro-average on
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.
* Enhancement: Added beta parameter to
metrics.homogeneity_completeness_v_measure and
metrics.v_measure_score to configure the
tradeoff between homogeneity and completeness.
* Fix: The metric metrics.r2_score is degenerate with a single sample
and now it returns NaN and raises exceptions.UndefinedMetricWarning..
* Fix: Fixed a bug where metrics.brier_score_loss will sometimes
return incorrect result when there's only one class in y_true..
* Fix: Fixed a bug in metrics.label_ranking_average_precision_score
where sample_weight wasn't taken into account for samples with degenerate
labels..
* API: The parameter labels in metrics.hamming_loss is deprecated
in version 0.21 and will be removed in version 0.23.
* Fix: The function 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.
* API: metrics.jaccard_similarity_score is deprecated in favour of
the more consistent metrics.jaccard_score. The former behavior for
binary and multiclass targets is broken..
+ sklearn.mixture
* Fix: Fixed a bug in mixture.BaseMixture and therefore on estimators
based on it, i.e. mixture.GaussianMixture and
mixture.BayesianGaussianMixture, where fit_predict and
fit.predict were not equivalent..
+ sklearn.model_selection
* Feature: Classes ~model_selection.GridSearchCV and
~model_selection.RandomizedSearchCV now allow for refit=callable
to add flexibility in identifying the best estimator.
See sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py.
* Enhancement: Classes ~model_selection.GridSearchCV,
~model_selection.RandomizedSearchCV, and methods
~model_selection.cross_val_score,
~model_selection.cross_val_predict,
~model_selection.cross_validate, now print train scores when
return_train_scores is True and verbose > 2. For
~model_selection.learning_curve, and
~model_selection.validation_curve only the latter is required.
* Enhancement: Some CV splitter classes and
model_selection.train_test_split now raise ValueError when the
resulting training set is empty..
* Fix: Fixed a bug where model_selection.StratifiedKFold
shuffles each class's samples with the same random_state,
making shuffle=True ineffective..
* Fix: Added ability for model_selection.cross_val_predict to handle
multi-label (and multioutput-multiclass) targets with predict_proba-type
methods..
* Fix: Fixed an issue in ~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.
+ sklearn.multiclass
* Fix: Fixed an issue in 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..
+ sklearn.multioutput
* Fix: Fixed a bug in multioutput.MultiOutputClassifier where the
predict_proba method incorrectly checked for predict_proba attribute in
the estimator object.
+ sklearn.neighbors
* MajorFeature: Added neighbors.NeighborhoodComponentsAnalysis for
metric learning, which implements the Neighborhood Components Analysis
algorithm.
* API: Methods in neighbors.NearestNeighbors :
~neighbors.NearestNeighbors.kneighbors,
~neighbors.NearestNeighbors.radius_neighbors,
~neighbors.NearestNeighbors.kneighbors_graph,
~neighbors.NearestNeighbors.radius_neighbors_graph
now raise NotFittedError, rather than AttributeError,
when called before fit.
+ sklearn.neural_network
* Fix: Fixed a bug in neural_network.MLPClassifier and
neural_network.MLPRegressor where the option shuffle=False
was being ignored..
* Fix: Fixed a bug in neural_network.MLPClassifier where
validation sets for early stopping were not sampled with stratification. In
the multilabel case however, splits are still not stratified..
+ sklearn.pipeline
* Feature: 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..
* Feature: Added optional parameter verbose in pipeline.Pipeline,
compose.ColumnTransformer and pipeline.FeatureUnion
and corresponding make_ helpers for showing progress and timing of
each step.
* Enhancement: pipeline.Pipeline now supports using 'passthrough'
as a transformer, with the same effect as None..
* Enhancement: pipeline.Pipeline implements __len__ and
therefore len(pipeline) returns the number of steps in the pipeline..
+ sklearn.preprocessing
* Feature: preprocessing.OneHotEncoder now supports dropping one
feature per category with a new drop parameter..
* Efficiency: preprocessing.OneHotEncoder and
preprocessing.OrdinalEncoder now handle pandas DataFrames more
efficiently..
* Efficiency: Make preprocessing.MultiLabelBinarizer cache class
mappings instead of calculating it every time on the fly.
* Efficiency: 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..
* Efficiency: Speed improvement in preprocessing.PolynomialFeatures,
in the dense case. Also added a new parameter order which controls output
order for further speed performances..
* Fix: Fixed the calculation overflow when using a float16 dtype with
preprocessing.StandardScaler.
* Fix: Fixed a bug in preprocessing.QuantileTransformer and
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..
* API: The default value of copy in 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
preprocessing and prevent unexpected side effects by modifying
the value of X inplace..
+ sklearn.svm
* Fix: Fixed an issue in 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..
+ 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..
* Feature: Decision Trees can now be exported in a human readable
textual format using tree.export_text.
* Feature: get_n_leaves() and get_depth() have been added to
tree.BaseDecisionTree and consequently all estimators based
on it, including tree.DecisionTreeClassifier,
tree.DecisionTreeRegressor, tree.ExtraTreeClassifier,
and tree.ExtraTreeRegressor..
* Fix: Trees and forests did not previously predict multi-output
classification targets with string labels, despite accepting them in fit..
* Fix: Fixed an issue with tree.BaseDecisionTree
and consequently all estimators based
on it, including tree.DecisionTreeClassifier,
tree.DecisionTreeRegressor, tree.ExtraTreeClassifier,
and 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..
+ sklearn.utils
* Feature: utils.resample now accepts a stratify parameter for
sampling according to class distributions..
* API: Deprecated warn_on_dtype parameter from utils.check_array
and utils.check_X_y. Added explicit warning for dtype conversion
in check_pairwise_arrays if the metric being passed is a
pairwise boolean metric..
+ 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
sklearn.set_config..
* 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.
* Efficiency: Memory copies are avoided when casting arrays to a different
dtype in multiple estimators..
* Fix: Fixed a bug in the implementation of the our_rand_r
helper function that was not behaving consistently across platforms.
+ 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..
+ Changes to estimator checks
These changes mostly affect library developers.
* Add check_fit_idempotent to
~utils.estimator_checks.check_estimator, which checks that
when fit is called twice with the same data, the ouput of
predict, predict_proba, transform, and decision_function does not
change.
* Many checks can now be disabled or configured with estimator_tags..
- Switch off tests, gh#scikit-learn/scikit-learn#12369
- Update to 0.20.2:
* This is a bug-fix release with some minor documentation
improvements and enhancements to features released in 0.20.0.
Note that we also include some API changes in this release, so
you might get some extra warnings after updating from 0.20.0.
- update to 0.20.0: * http://scikit-learn.org/stable/whats_new.html#version-0-20-0 Support for Python 3.3 has been officially dropped - drop scikit-learn-skip-test.patch (merged)
- Skip test sklearn.linear_model.tests.test_logistic.test_max_iter * Upstream plans to fix it in next release * scikit-learn-skip-test.patch
- Update package to properly state dependencies as in setup.py - Install license file
- update to version 0.19.1:
* API changes
+ Reverted the addition of metrics.ndcg_score and
metrics.dcg_score which had been merged into version 0.19.0 by
error. The implementations were broken and undocumented.
+ return_train_score which was added to
model_selection.GridSearchCV, model_selection.RandomizedSearchCV
and model_selection.cross_validate in version 0.19.0 will be
changing its default value from True to False in version
0.21. We found that calculating training score could have a
great effect on cross validation runtime in some cases. Users
should explicitly set return_train_score to False if prediction
or scoring functions are slow, resulting in a deleterious effect
on CV runtime, or to True if they wish to use the calculated
scores. #9677 by Kumar Ashutosh and Joel Nothman.
+ correlation_models and regression_models from the legacy
gaussian processes implementation have been belatedly
deprecated. #9717 by Kumar Ashutosh.
* Bug fixes
+ Avoid integer overflows in metrics.matthews_corrcoef. #9693 by
Sam Steingold.
+ Fix ValueError in preprocessing.LabelEncoder when using
inverse_transform on unseen labels. #9816 by Charlie Newey.
+ Fixed a bug in the objective function for manifold.TSNE (both
exact and with the Barnes-Hut approximation) when n_components
>= 3. #9711 by @goncalo-rodrigues.
+ Fix regression in model_selection.cross_val_predict where it
raised an error with method='predict_proba' for some
probabilistic classifiers. #9641 by James Bourbeau.
+ Fixed a bug where datasets.make_classification modified its
input weights. #9865 by Sachin Kelkar.
+ model_selection.StratifiedShuffleSplit now works with
multioutput multiclass or multilabel data with more than 1000
columns. #9922 by Charlie Brummitt.
+ Fixed a bug with nested and conditional parameter setting,
e.g. setting a pipeline step and its parameter at the same
time. #9945 by Andreas Müller and Joel Nothman.
* Regressions in 0.19.0 fixed in 0.19.1:
+ Fixed a bug where parallelised prediction in random forests was
not thread-safe and could (rarely) result in arbitrary
errors. #9830 by Joel Nothman.
+ Fix regression in model_selection.cross_val_predict where it no
longer accepted X as a list. #9600 by Rasul Kerimov.
+ Fixed handling of model_selection.cross_val_predict for binary
classification with method='decision_function'. #9593 by
Reiichiro Nakano and core devs.
+ Fix regression in pipeline.Pipeline where it no longer accepted
steps as a tuple. #9604 by Joris Van den Bossche.
+ Fix bug where n_iter was not properly deprecated, leaving n_iter
unavailable for interim use in linear_model.SGDClassifier,
linear_model.SGDRegressor,
linear_model.PassiveAggressiveClassifier,
linear_model.PassiveAggressiveRegressor and
linear_model.Perceptron. #9558 by Andreas Müller.
+ Dataset fetchers make sure temporary files are closed before
removing them, which caused errors on Windows. #9847 by Joan
Massich.
+ Fixed a regression in manifold.TSNE where it no longer supported
metrics other than ‘euclidean’ and ‘precomputed’. #9623 by Oli
Blum.
* Enhancements
+ Our test suite and utils.estimator_checks.check_estimators can
now be run without Nose installed. #9697 by Joan Massich.
+ To improve usability of version 0.19’s pipeline.Pipeline
caching, memory now allows joblib.Memory instances. This make
use of the new utils.validation.check_memory helper. #9584 by
Kumar Ashutosh
+ Some fixes to examples: #9750, #9788, #9815
+ Made a FutureWarning in SGD-based estimators less verbose. #9802
by Vrishank Bhardwaj.
- update to version 0.19.0:
* Highlights
+ We are excited to release a number of great new features
including neighbors.LocalOutlierFactor for anomaly detection,
preprocessing.QuantileTransformer for robust feature
transformation, and the multioutput.ClassifierChain
meta-estimator to simply account for dependencies between
classes in multilabel problems. We have some new algorithms in
existing estimators, such as multiplicative update in
decomposition.NMF and multinomial
linear_model.LogisticRegression with L1 loss (use
solver='saga').
+ Cross validation is now able to return the results from multiple
metric evaluations. The new model_selection.cross_validate can
return many scores on the test data as well as training set
performance and timings, and we have extended the scoring and
refit parameters for grid/randomized search to handle multiple
metrics.
+ You can also learn faster. For instance, the new option to cache
transformations in pipeline.Pipeline makes grid search over
pipelines including slow transformations much more
efficient. And you can predict faster: if you’re sure you know
what you’re doing, you can turn off validating that the input is
finite using config_context.
+ We’ve made some important fixes too. We’ve fixed a longstanding
implementation error in metrics.average_precision_score, so
please be cautious with prior results reported from that
function. A number of errors in the manifold.TSNE implementation
have been fixed, particularly in the default Barnes-Hut
approximation. semi_supervised.LabelSpreading and
semi_supervised.LabelPropagation have had substantial
fixes. LabelPropagation was previously broken. LabelSpreading
should now correctly respect its alpha parameter.
* Changed models
* The following estimators and functions, when fit with the same
data and parameters, may produce different models from the
previous version. This often occurs due to changes in the
modelling logic (bug fixes or enhancements), or in random sampling
procedures.
+ cluster.KMeans with sparse X and initial centroids given (bug
fix)
+ cross_decomposition.PLSRegression with scale=True (bug fix)
+ ensemble.GradientBoostingClassifier and
ensemble.GradientBoostingRegressor where min_impurity_split is
used (bug fix)
+ gradient boosting loss='quantile' (bug fix)
+ ensemble.IsolationForest (bug fix)
+ feature_selection.SelectFdr (bug fix)
+ linear_model.RANSACRegressor (bug fix)
+ linear_model.LassoLars (bug fix)
+ linear_model.LassoLarsIC (bug fix)
+ manifold.TSNE (bug fix)
+ neighbors.NearestCentroid (bug fix)
+ semi_supervised.LabelSpreading (bug fix)
+ semi_supervised.LabelPropagation (bug fix)
+ tree based models where min_weight_fraction_leaf is used
(enhancement)
* complete changelog at http://scikit-learn.org/stable/whats_new.html
- Implement single-spec version
- Update source URL
- Update to version 0.18.1
* Large number of changes. See:
https://github.com/scikit-learn/scikit-learn/blob/0.18.1/doc/whats_new.rst
- Switch to proper package name: python-scikit-learn
- Update to version 0.17
- Update to version 14.1
* Minor bugfixes
- Update to version 14.0
* Changelog
- Missing values with sparse and dense matrices can be imputed with the
transformer :class:`preprocessing.Imputer` by `Nicolas Trésegnie`_.
- The core implementation of decisions trees has been rewritten from
scratch, allowing for faster tree induction and lower memory
consumption in all tree-based estimators. By `Gilles Louppe`_.
- Added :class:`ensemble.AdaBoostClassifier` and
:class:`ensemble.AdaBoostRegressor`, by `Noel Dawe`_ and
`Gilles Louppe`_. See the :ref:`AdaBoost <adaboost>` section of the user
guide for details and examples.
- Added :class:`grid_search.RandomizedSearchCV` and
:class:`grid_search.ParameterSampler` for randomized hyperparameter
optimization. By `Andreas Müller`_.
- Added :ref:`biclustering <biclustering>` algorithms
(:class:`sklearn.cluster.bicluster.SpectralCoclustering` and
:class:`sklearn.cluster.bicluster.SpectralBiclustering`), data
generation methods (:func:`sklearn.datasets.make_biclusters` and
:func:`sklearn.datasets.make_checkerboard`), and scoring metrics
(:func:`sklearn.metrics.consensus_score`). By `Kemal Eren`_.
- Added :ref:`Restricted Boltzmann Machines<rbm>`
(:class:`neural_network.BernoulliRBM`). By `Yann Dauphin`_.
- Python 3 support by `Justin Vincent`_, `Lars Buitinck`_,
`Subhodeep Moitra`_ and `Olivier Grisel`_. All tests now pass under
Python 3.3.
- Ability to pass one penalty (alpha value) per target in
:class:`linear_model.Ridge`, by @eickenberg and `Mathieu Blondel`_.
- Fixed :mod:`sklearn.linear_model.stochastic_gradient.py` L2 regularization
issue (minor practical significants).
By `Norbert Crombach`_ and `Mathieu Blondel`_ .
- Added an interactive version of `Andreas Müller`_'s
`Machine Learning Cheat Sheet (for scikit-learn)
<http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html>`_
to the documentation. See :ref:`Choosing the right estimator <ml_map>`.
By `Jaques Grobler`_.
- :class:`grid_search.GridSearchCV` and
:func:`cross_validation.cross_val_score` now support the use of advanced
scoring function such as area under the ROC curve and f-beta scores.
See :ref:`scoring_parameter` for details. By `Andreas Müller`_
and `Lars Buitinck`_.
Passing a function from :mod:`sklearn.metrics` as ``score_func`` is
deprecated.
- Multi-label classification output is now supported by
:func:`metrics.accuracy_score`, :func:`metrics.zero_one_loss`,
:func:`metrics.f1_score`, :func:`metrics.fbeta_score`,
:func:`metrics.classification_report`,
:func:`metrics.precision_score` and :func:`metrics.recall_score`
by `Arnaud Joly`_.
- Two new metrics :func:`metrics.hamming_loss` and
:func:`metrics.jaccard_similarity_score`
are added with multi-label support by `Arnaud Joly`_.
- Speed and memory usage improvements in
:class:`feature_extraction.text.CountVectorizer` and
:class:`feature_extraction.text.TfidfVectorizer`,
by Jochen Wersdörfer and Roman Sinayev.
- The ``min_df`` parameter in
:class:`feature_extraction.text.CountVectorizer` and
:class:`feature_extraction.text.TfidfVectorizer`, which used to be 2,
has been reset to 1 to avoid unpleasant surprises (empty vocabularies)
for novice users who try it out on tiny document collections.
A value of at least 2 is still recommended for practical use.
- :class:`svm.LinearSVC`, :class:`linear_model.SGDClassifier` and
:class:`linear_model.SGDRegressor` now have a ``sparsify`` method that
converts their ``coef_`` into a sparse matrix, meaning stored models
trained using these estimators can be made much more compact.
- :class:`linear_model.SGDClassifier` now produces multiclass probability
estimates when trained under log loss or modified Huber loss.
- Hyperlinks to documentation in example code on the website by
`Martin Luessi`_.
- Fixed bug in :class:`preprocessing.MinMaxScaler` causing incorrect scaling
of the features for non-default ``feature_range`` settings. By `Andreas
Müller`_.
- ``max_features`` in :class:`tree.DecisionTreeClassifier`,
:class:`tree.DecisionTreeRegressor` and all derived ensemble estimators
now supports percentage values. By `Gilles Louppe`_.
- Performance improvements in :class:`isotonic.IsotonicRegression` by
`Nelle Varoquaux`_.
- :func:`metrics.accuracy_score` has an option normalize to return
the fraction or the number of correctly classified sample
by `Arnaud Joly`_.
- Added :func:`metrics.log_loss` that computes log loss, aka cross-entropy
loss. By Jochen Wersdörfer and `Lars Buitinck`_.
- A bug that caused :class:`ensemble.AdaBoostClassifier`'s to output
incorrect probabilities has been fixed.
- Feature selectors now share a mixin providing consistent `transform`,
`inverse_transform` and `get_support` methods. By `Joel Nothman`_.
- A fitted :class:`grid_search.GridSearchCV` or
:class:`grid_search.RandomizedSearchCV` can now generally be pickled.
By `Joel Nothman`_.
- Refactored and vectorized implementation of :func:`metrics.roc_curve`
and :func:`metrics.precision_recall_curve`. By `Joel Nothman`_.
- The new estimator :class:`sklearn.decomposition.TruncatedSVD`
performs dimensionality reduction using SVD on sparse matrices,
and can be used for latent semantic analysis (LSA).
By `Lars Buitinck`_.
- Added self-contained example of out-of-core learning on text data
:ref:`example_applications_plot_out_of_core_classification.py`.
By `Eustache Diemert`_.
- The default number of components for
:class:`sklearn.decomposition.RandomizedPCA` is now correctly documented
to be ``n_features``. This was the default behavior, so programs using it
will continue to work as they did.
- :class:`sklearn.cluster.KMeans` now fits several orders of magnitude
faster on sparse data (the speedup depends on the sparsity). By
`Lars Buitinck`_.
- Reduce memory footprint of FastICA by `Denis Engemann`_ and
`Alexandre Gramfort`_.
- Verbose output in :mod:`sklearn.ensemble.gradient_boosting` now uses
a column format and prints progress in decreasing frequency.
It also shows the remaining time. By `Peter Prettenhofer`_.
- :mod:`sklearn.ensemble.gradient_boosting` provides out-of-bag improvement
:attr:`~sklearn.ensemble.GradientBoostingRegressor.oob_improvement_`
rather than the OOB score for model selection. An example that shows
how to use OOB estimates to select the number of trees was added.
By `Peter Prettenhofer`_.
- Most metrics now support string labels for multiclass classification
by `Arnaud Joly`_ and `Lars Buitinck`_.
- New OrthogonalMatchingPursuitCV class by `Alexandre Gramfort`_
and `Vlad Niculae`_.
- Fixed a bug in :class:`sklearn.covariance.GraphLassoCV`: the
'alphas' parameter now works as expected when given a list of
values. By Philippe Gervais.
- Fixed an important bug in :class:`sklearn.covariance.GraphLassoCV`
that prevented all folds provided by a CV object to be used (only
the first 3 were used). When providing a CV object, execution
time may thus increase significantly compared to the previous
version (bug results are correct now). By Philippe Gervais.
- :class:`cross_validation.cross_val_score` and the :mod:`grid_search`
module is now tested with multi-output data by `Arnaud Joly`_.
- :func:`datasets.make_multilabel_classification` can now return
the output in label indicator multilabel format by `Arnaud Joly`_.
- K-nearest neighbors, :class:`neighbors.KNeighborsRegressor`
and :class:`neighbors.RadiusNeighborsRegressor`,
and radius neighbors, :class:`neighbors.RadiusNeighborsRegressor` and
:class:`neighbors.RadiusNeighborsClassifier` support multioutput data
by `Arnaud Joly`_.
- Random state in LibSVM-based estimators (:class:`svm.SVC`, :class:`NuSVC`,
:class:`OneClassSVM`, :class:`svm.SVR`, :class:`svm.NuSVR`) can now be
controlled. This is useful to ensure consistency in the probability
estimates for the classifiers trained with ``probability=True``. By
`Vlad Niculae`_.
- Out-of-core learning support for discrete naive Bayes classifiers
:class:`sklearn.naive_bayes.MultinomialNB` and
:class:`sklearn.naive_bayes.BernoulliNB` by adding the ``partial_fit``
method by `Olivier Grisel`_.
- New website design and navigation by `Gilles Louppe`_, `Nelle Varoquaux`_,
Vincent Michel and `Andreas Müller`_.
- Improved documentation on :ref:`multi-class, multi-label and multi-output
classification <multiclass>` by `Yannick Schwartz`_ and `Arnaud Joly`_.
- Better input and error handling in the :mod:`metrics` module by
`Arnaud Joly`_ and `Joel Nothman`_.
- Speed optimization of the :mod:`hmm` module by `Mikhail Korobov`_
- Significant speed improvements for :class:`sklearn.cluster.DBSCAN`_
by `cleverless <https://github.com/cleverless>`_
* API changes:
- The :func:`auc_score` was renamed :func:`roc_auc_score`.
- Testing scikit-learn with `sklearn.test()` is deprecated. Use
`nosetest sklearn` from the command line.
- Feature importances in :class:`tree.DecisionTreeClassifier`,
:class:`tree.DecisionTreeRegressor` and all derived ensemble estimators
are now computed on the fly when accessing the ``feature_importances_``
attribute. Setting ``compute_importances=True`` is no longer required.
By `Gilles Louppe`_.
- :class:`linear_model.lasso_path` and
:class:`linear_model.enet_path` can return its results in the same
format as that of :class:`linear_model.lars_path`. This is done by
setting the `return_models` parameter to `False`. By
`Jaques Grobler`_ and `Alexandre Gramfort`_
- :class:`grid_search.IterGrid` was renamed to
:class:`grid_search.ParameterGrid`.
- Fixed bug in :class:`KFold` causing imperfect class balance in some
cases. By `Alexandre Gramfort`_ and Tadej Janež.
- :class:`sklearn.neighbors.BallTree` has been refactored, and a
:class:`sklearn.neighbors.KDTree` has been
added which shares the same interface. The Ball Tree now works with
a wide variety of distance metrics. Both classes have many new
methods, including single-tree and dual-tree queries, breadth-first
and depth-first searching, and more advanced queries such as
kernel density estimation and 2-point correlation functions.
By `Jake Vanderplas`_
- Support for scipy.spatial.cKDTree within neighbors queries has been
removed, and the functionality replaced with the new :class:`KDTree`
class.
- :class:`sklearn.neighbors.KernelDensity` has been added, which performs
efficient kernel density estimation with a variety of kernels.
- :class:`sklearn.decomposition.KernelPCA` now always returns output with
``n_components`` components, unless the new parameter ``remove_zero_eig``
is set to ``True``. This new behavior is consistent with the way
kernel PCA was always documented; previously, the removal of components
with zero eigenvalues was tacitly performed on all data.
- ``gcv_mode="auto"`` no longer tries to perform SVD on a densified
sparse matrix in :class:`sklearn.linear_model.RidgeCV`.
- Sparse matrix support in :class:`sklearn.decomposition.RandomizedPCA`
is now deprecated in favor of the new ``TruncatedSVD``.
- :class:`cross_validation.KFold` and
:class:`cross_validation.StratifiedKFold` now enforce `n_folds >= 2`
otherwise a ``ValueError`` is raised. By `Olivier Grisel`_.
- :func:`datasets.load_files`'s ``charset`` and ``charset_errors``
parameters were renamed ``encoding`` and ``decode_errors``.
- Attribute ``oob_score_`` in :class:`sklearn.ensemble.GradientBoostingRegressor`
and :class:`sklearn.ensemble.GradientBoostingClassifier`
is deprecated and has been replaced by ``oob_improvement_`` .
- Attributes in OrthogonalMatchingPursuit have been deprecated
(copy_X, Gram, ...) and precompute_gram renamed precompute
for consistency. See #2224.
- :class:`sklearn.preprocessing.StandardScaler` now converts integer input
to float, and raises a warning. Previously it rounded for dense integer
input.
- Better input validation, warning on unexpected shapes for y.
- Fix building on 13.1+
- Update BuildRequires
- Cleanup spec file formatting
- Require python-setuptools instead of distribute (upstreams merged)
- Update to version 0.13.1
- Update to version 0.12.1
- Clean up spec file - Update to version 0.11
- remove unneeded libatals3-devel dependency
- fix python-Sphinx requirement
- first package - version 0.5