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# Release Highlights for scikit-learn 0.22

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 release notes.

To install the latest version (with pip):

    pip install --upgrade scikit-learn

or with conda:

    conda install -c conda-forge 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 `plot_roc_curve`, but other plots utilities are supported like `plot_partial_dependence`, `plot_precision_recall_curve`, and `plot_confusion_matrix`. Read more about this new API in the User Guide.

    import matplotlib.pyplot as plt

    from sklearn.datasets import make_classification
    from sklearn.ensemble import RandomForestClassifier

    # from sklearn.metrics import plot_roc_curve
    from sklearn.metrics import RocCurveDisplay
    from sklearn.model_selection import train_test_split
    from sklearn.svm import SVC

    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)

    # plot_roc_curve has been removed in version 1.2. From 1.2, use RocCurveDisplay instead.
    # svc_disp = plot_roc_curve(svc, X_test, y_test)
    # rfc_disp = plot_roc_curve(rfc, X_test, y_test, ax=svc_disp.ax_)
    svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
    rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=svc_disp.ax_)
    rfc_disp.figure_.suptitle("ROC curve comparison")

    plt.show()

## Stacking Classifier and Regressor

`StackingClassifier` and `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 User Guide.

    from sklearn.datasets import load_iris
    from sklearn.ensemble import StackingClassifier
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.svm import LinearSVC

    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)

    /root/scikit-learn-1.3.2/sklearn/svm/_classes.py:32: FutureWarning:

    The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.

    /root/scikit-learn-1.3.2/sklearn/svm/_classes.py:32: FutureWarning:

    The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.

    /root/scikit-learn-1.3.2/sklearn/svm/_classes.py:32: FutureWarning:

    The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.

    /root/scikit-learn-1.3.2/sklearn/svm/_classes.py:32: FutureWarning:

    The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.

    /root/scikit-learn-1.3.2/sklearn/svm/_classes.py:32: FutureWarning:

    The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.

    /root/scikit-learn-1.3.2/sklearn/svm/_classes.py:32: FutureWarning:

    The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.

    0.9473684210526315

## Permutation-based feature importance

The `inspection.permutation_importance` can be used to get an estimate of the importance of each feature, for any fitted estimator:

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import make_classification
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.inspection import permutation_importance

    X, y = make_classification(random_state=0, n_features=5, n_informative=3)
    feature_names = np.array([f"x_{i}" for i in range(X.shape[1])])

    rf = RandomForestClassifier(random_state=0).fit(X, y)
    result = permutation_importance(rf, X, y, n_repeats=10, random_state=0, n_jobs=2)

    fig, ax = plt.subplots()
    sorted_idx = result.importances_mean.argsort()
    ax.boxplot(
        result.importances[sorted_idx].T, vert=False, labels=feature_names[sorted_idx]
    )
    ax.set_title("Permutation Importance of each feature")
    ax.set_ylabel("Features")
    fig.tight_layout()
    plt.show()

## Native support for missing values for gradient boosting

The `ensemble.HistGradientBoostingClassifier` and `ensemble.HistGradientBoostingRegressor` now have native support for missing values (NaNs). This means that there is no need for imputing data when training or predicting.

    from sklearn.ensemble import HistGradientBoostingClassifier

    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))

    [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, `neighbors.KNeighborsTransformer` and `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 User Guide.

    from tempfile import TemporaryDirectory

    from sklearn.manifold import Isomap
    from sklearn.neighbors import KNeighborsTransformer
    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, `nan_euclidean_distances`, is used to find the nearest neighbors.

Read more in the User Guide.

    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))

    [[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 User Guide for details.

    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_])
        )
    )

    Average number of nodes without pruning 22.3
    Average number of nodes with pruning 6.4

## Retrieve dataframes from OpenML

`datasets.fetch_openml` can now return pandas dataframe and thus properly handle datasets with heterogeneous data:

    from sklearn.datasets import fetch_openml

    titanic = fetch_openml("titanic", version=1, as_frame=True, parser="pandas")
    print(titanic.data.head()[["pclass", "embarked"]])

       pclass embarked
    0       1        S
    1       1        S
    2       1        S
    3       1        S
    4       1        S

## Checking scikit-learn compatibility of an estimator

Developers can check the compatibility of their scikit-learn compatible estimators using `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.

..note::

  This entry was slightly updated in version 0.24, where passing classes isn’t supported anymore: pass instances instead.

    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 `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 User Guide.

    from sklearn.datasets import make_classification
    from sklearn.metrics import roc_auc_score
    from sklearn.svm import SVC

    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"))

    0.9965333333333333

Total running time of the script: (0 minutes 1.251 seconds)

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