.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_22_0.py: ======================================== Release Highlights for scikit-learn 0.22 ======================================== .. currentmodule:: sklearn We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes and new features! We detail below a few of the major features of this release. For an exhaustive list of all the changes, please refer to the :ref:`release notes `. To install the latest version (with pip):: pip install --upgrade scikit-learn or with conda:: conda install scikit-learn New plotting API ---------------- A new plotting API is available for creating visualizations. This new API allows for quickly adjusting the visuals of a plot without involving any recomputation. It is also possible to add different plots to the same figure. The following example illustrates :class:`~metrics.plot_roc_curve`, but other plots utilities are supported like :class:`~inspection.plot_partial_dependence`, :class:`~metrics.plot_precision_recall_curve`, and :class:`~metrics.plot_confusion_matrix`. Read more about this new API in the :ref:`User Guide `. .. code-block:: default from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import plot_roc_curve from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification import matplotlib.pyplot as plt X, y = make_classification(random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) svc = SVC(random_state=42) svc.fit(X_train, y_train) rfc = RandomForestClassifier(random_state=42) rfc.fit(X_train, y_train) svc_disp = plot_roc_curve(svc, X_test, y_test) rfc_disp = plot_roc_curve(rfc, X_test, y_test, ax=svc_disp.ax_) rfc_disp.figure_.suptitle("ROC curve comparison") plt.show() .. image:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_22_0_001.png :class: sphx-glr-single-img Stacking Classifier and Regressor --------------------------------- :class:`~ensemble.StackingClassifier` and :class:`~ensemble.StackingRegressor` allow you to have a stack of estimators with a final classifier or a regressor. Stacked generalization consists in stacking the output of individual estimators and use a classifier to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator. Base estimators are fitted on the full ``X`` while the final estimator is trained using cross-validated predictions of the base estimators using ``cross_val_predict``. Read more in the :ref:`User Guide `. .. code-block:: default from sklearn.datasets import load_iris from sklearn.svm import LinearSVC from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler from sklearn.pipeline import make_pipeline from sklearn.ensemble import StackingClassifier from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) estimators = [ ('rf', RandomForestClassifier(n_estimators=10, random_state=42)), ('svr', make_pipeline(StandardScaler(), LinearSVC(random_state=42))) ] clf = StackingClassifier( estimators=estimators, final_estimator=LogisticRegression() ) X_train, X_test, y_train, y_test = train_test_split( X, y, stratify=y, random_state=42 ) clf.fit(X_train, y_train).score(X_test, y_test) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 0.9473684210526315 Permutation-based feature importance ------------------------------------ The :func:`inspection.permutation_importance` can be used to get an estimate of the importance of each feature, for any fitted estimator: .. code-block:: default from sklearn.ensemble import RandomForestClassifier from sklearn.inspection import permutation_importance X, y = make_classification(random_state=0, n_features=5, n_informative=3) rf = RandomForestClassifier(random_state=0).fit(X, y) result = permutation_importance(rf, X, y, n_repeats=10, random_state=0, n_jobs=-1) fig, ax = plt.subplots() sorted_idx = result.importances_mean.argsort() ax.boxplot(result.importances[sorted_idx].T, vert=False, labels=range(X.shape[1])) ax.set_title("Permutation Importance of each feature") ax.set_ylabel("Features") fig.tight_layout() plt.show() .. image:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_22_0_002.png :class: sphx-glr-single-img Native support for missing values for gradient boosting ------------------------------------------------------- The :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` now have native support for missing values (NaNs). This means that there is no need for imputing data when training or predicting. .. code-block:: default from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingClassifier import numpy as np X = np.array([0, 1, 2, np.nan]).reshape(-1, 1) y = [0, 0, 1, 1] gbdt = HistGradientBoostingClassifier(min_samples_leaf=1).fit(X, y) print(gbdt.predict(X)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none [0 0 1 1] Precomputed sparse nearest neighbors graph ------------------------------------------ Most estimators based on nearest neighbors graphs now accept precomputed sparse graphs as input, to reuse the same graph for multiple estimator fits. To use this feature in a pipeline, one can use the `memory` parameter, along with one of the two new transformers, :class:`neighbors.KNeighborsTransformer` and :class:`neighbors.RadiusNeighborsTransformer`. The precomputation can also be performed by custom estimators to use alternative implementations, such as approximate nearest neighbors methods. See more details in the :ref:`User Guide `. .. code-block:: default from tempfile import TemporaryDirectory from sklearn.neighbors import KNeighborsTransformer from sklearn.manifold import Isomap from sklearn.pipeline import make_pipeline X, y = make_classification(random_state=0) with TemporaryDirectory(prefix="sklearn_cache_") as tmpdir: estimator = make_pipeline( KNeighborsTransformer(n_neighbors=10, mode='distance'), Isomap(n_neighbors=10, metric='precomputed'), memory=tmpdir) estimator.fit(X) # We can decrease the number of neighbors and the graph will not be # recomputed. estimator.set_params(isomap__n_neighbors=5) estimator.fit(X) KNN Based Imputation ------------------------------------ We now support imputation for completing missing values using k-Nearest Neighbors. Each sample's missing values are imputed using the mean value from ``n_neighbors`` nearest neighbors found in the training set. Two samples are close if the features that neither is missing are close. By default, a euclidean distance metric that supports missing values, :func:`~metrics.nan_euclidean_distances`, is used to find the nearest neighbors. Read more in the :ref:`User Guide `. .. code-block:: default import numpy as np from sklearn.impute import KNNImputer X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]] imputer = KNNImputer(n_neighbors=2) print(imputer.fit_transform(X)) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none [[1. 2. 4. ] [3. 4. 3. ] [5.5 6. 5. ] [8. 8. 7. ]] Tree pruning ------------ It is now possible to prune most tree-based estimators once the trees are built. The pruning is based on minimal cost-complexity. Read more in the :ref:`User Guide ` for details. .. code-block:: default X, y = make_classification(random_state=0) rf = RandomForestClassifier(random_state=0, ccp_alpha=0).fit(X, y) print("Average number of nodes without pruning {:.1f}".format( np.mean([e.tree_.node_count for e in rf.estimators_]))) rf = RandomForestClassifier(random_state=0, ccp_alpha=0.05).fit(X, y) print("Average number of nodes with pruning {:.1f}".format( np.mean([e.tree_.node_count for e in rf.estimators_]))) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Average number of nodes without pruning 22.3 Average number of nodes with pruning 6.4 Retrieve dataframes from OpenML ------------------------------- :func:`datasets.fetch_openml` can now return pandas dataframe and thus properly handle datasets with heterogeneous data: .. code-block:: default from sklearn.datasets import fetch_openml titanic = fetch_openml('titanic', version=1, as_frame=True) print(titanic.data.head()[['pclass', 'embarked']]) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none pclass embarked 0 1.0 S 1 1.0 S 2 1.0 S 3 1.0 S 4 1.0 S Checking scikit-learn compatibility of an estimator --------------------------------------------------- Developers can check the compatibility of their scikit-learn compatible estimators using :func:`~utils.estimator_checks.check_estimator`. For instance, the ``check_estimator(LinearSVC)`` passes. We now provide a ``pytest`` specific decorator which allows ``pytest`` to run all checks independently and report the checks that are failing. .. code-block:: default from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeRegressor from sklearn.utils.estimator_checks import parametrize_with_checks @parametrize_with_checks([LogisticRegression, DecisionTreeRegressor]) def test_sklearn_compatible_estimator(estimator, check): check(estimator) ROC AUC now supports multiclass classification ---------------------------------------------- The :func:`roc_auc_score` function can also be used in multi-class classification. Two averaging strategies are currently supported: the one-vs-one algorithm computes the average of the pairwise ROC AUC scores, and the one-vs-rest algorithm computes the average of the ROC AUC scores for each class against all other classes. In both cases, the multiclass ROC AUC scores are computed from the probability estimates that a sample belongs to a particular class according to the model. The OvO and OvR algorithms support weighting uniformly (``average='macro'``) and weighting by the prevalence (``average='weighted'``). Read more in the :ref:`User Guide `. .. code-block:: default from sklearn.datasets import make_classification from sklearn.svm import SVC from sklearn.metrics import roc_auc_score X, y = make_classification(n_classes=4, n_informative=16) clf = SVC(decision_function_shape='ovo', probability=True).fit(X, y) print(roc_auc_score(y, clf.predict_proba(X), multi_class='ovo')) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 0.9957333333333332 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 4.730 seconds) **Estimated memory usage:** 8 MB .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_22_0.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_0_22_0.ipynb :width: 150 px .. container:: sphx-glr-download :download:`Download Python source code: plot_release_highlights_0_22_0.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_release_highlights_0_22_0.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_