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Update Info: Base Release
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Change Logs

* Thu May 07 2020 Christian Goll <>
- added provide sklearn and python-sklearn as this is name most
  users will search for
* Tue Apr 28 2020 Tomá? Chvátal <>
- Require at least pytest 4.x for testing
* Tue Jan 07 2020
- 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
* Tue Nov 19 2019 Todd R <>
- 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_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
  + 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.
* Fri Jul 26 2019 Todd R <>
- 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
  + 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
  + 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
  * 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
  * 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
  + 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
  + 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
  + 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
  * Fix:  The values of feature_importances_ in all random forest based
    models (i.e.
    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
  * 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.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
    >>> 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
  + sklearn.isotonic
  * Feature:  Allow different dtypes (such as float32) in
  + 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
  * 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
  * 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
  * 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.
  * Enhancement:  Classes ~model_selection.GridSearchCV,
    ~model_selection.RandomizedSearchCV, and methods
    ~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
  * 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
  * API:  Methods in neighbors.NearestNeighbors :
    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
  * 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
  * 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
  * 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
  * Many checks can now be disabled or configured with estimator_tags..
Version: 0.19.1-bp150.1.4
* Mon Oct 30 2017
- 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.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
    + Fixed a regression in manifold.TSNE where it no longer supported
    metrics other than ?euclidean? and ?precomputed?. #9623 by Oli
  * 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.
* Sun Sep 24 2017
- 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
    + 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
    + 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
    + cluster.KMeans with sparse X and initial centroids given (bug
    + 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
  * complete changelog at
* Sun Jun 11 2017
- Implement single-spec version
- Update source URL
- Update to version 0.18.1
  * Large number of changes. See:
* Mon Jan 11 2016
- Switch to proper package name: python-scikit-learn
* Fri Nov 20 2015 Angelos Tzotsos <>
- Update to version 0.17
* Thu Oct 24 2013
- 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:`` L2 regularization
    issue (minor practical significants).
    By `Norbert Crombach`_ and `Mathieu Blondel`_ .
  - Added an interactive version of `Andreas Müller`_'s
    `Machine Learning Cheat Sheet (for scikit-learn)
    to the documentation. See :ref:`Choosing the right estimator <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
  - 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.precision_score` and :func:`metrics.recall_score`
    by `Arnaud Joly`_.
  - Two new metrics :func:`metrics.hamming_loss` and
    are added with multi-label support by `Arnaud Joly`_.
  - Speed and memory usage improvements in
    :class:`feature_extraction.text.CountVectorizer` and
    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
  - ``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
    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
    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 <>`_
  * 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
  - Fixed bug in :class:`KFold` causing imperfect class balance in some
    cases. By `Alexandre Gramfort`_ and Tadej Jane?.
  - :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:`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
  - Better input validation, warning on unexpected shapes for y.
- Fix building on 13.1+
- Update BuildRequires
- Cleanup spec file formatting
* Thu Oct 24 2013
- Require python-setuptools instead of distribute (upstreams merged)
* Fri May 03 2013
- Update to version 0.13.1
* Sat Oct 13 2012 Angelos Tzotsos <>
- Update to version 0.12.1
* Sun Jun 03 2012
- Clean up spec file
- Update to version 0.11
* Wed Mar 07 2012
- remove unneeded libatals3-devel dependency
* Mon Oct 10 2011
- fix python-Sphinx requirement
* Sat Oct 23 2010
- first package
- version 0.5
Version: 0.20.2-bp151.2.2
* Wed Jan 30 2019 Matej Cepl <>
- Switch off tests, gh#scikit-learn/scikit-learn#12369
* Tue Jan 29 2019
- 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.
* Wed Oct 24 2018 Dirk Mueller <>
- update to 0.20.0:
  Support for Python 3.3 has been officially dropped
- drop scikit-learn-skip-test.patch (merged)
* Thu May 17 2018
- Skip test sklearn.linear_model.tests.test_logistic.test_max_iter
  * Upstream plans to fix it in next release
  * scikit-learn-skip-test.patch
* Thu May 17 2018
- Update package to properly state dependencies as in
- Install license file