Plot Release Highlights 0 22 0¶
======================================== 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 <release_notes_0_22>.
To install the latest version (with pip)::
pip install --upgrade scikit-learn
or with conda::
conda install -c conda-forge scikit-learn
Imports for scikit-learn 0.22 Release Highlights: Visualization API, Stacking, and Permutation Importance¶
This release introduced the Display-based plotting API (RocCurveDisplay, ConfusionMatrixDisplay), StackingClassifier/StackingRegressor for model ensembling, and permutation_importance for model-agnostic feature importance: The new visualization objects decouple computation from rendering, allowing ROC curves from multiple classifiers to be overlaid on the same axes without recomputing predictions. StackingClassifier implements stacked generalization where base estimator predictions (generated via cross_val_predict to avoid data leakage) become input features for a final meta-learner like LogisticRegression, combining the complementary strengths of diverse models such as RandomForestClassifier and LinearSVC.
Additional highlights include native NaN support in HistGradientBoostingClassifier, KNNImputer for nearest-neighbor missing value imputation, cost-complexity pruning via ccp_alpha, and KNeighborsTransformer for precomputed sparse neighbor graphs: permutation_importance measures each feature’s contribution by randomly shuffling its values and observing the drop in model score, providing a reliable alternative to tree-based feature_importances_ that avoids bias toward high-cardinality features. The ccp_alpha parameter enables minimal cost-complexity pruning, which iteratively removes branches that provide the smallest per-node improvement in impurity, producing simpler trees that generalize better.
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
# %%
# 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
# :ref:`User Guide <visualizations>`.
import matplotlib
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
from sklearn.utils.fixes import parse_version
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
# ---------------------------------
# :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 <stacking>`.
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(dual="auto", 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)
# %%
# 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:
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()
# `labels` argument in boxplot is deprecated in matplotlib 3.9 and has been
# renamed to `tick_labels`. The following code handles this, but as a
# scikit-learn user you probably can write simpler code by using `labels=...`
# (matplotlib < 3.9) or `tick_labels=...` (matplotlib >= 3.9).
tick_labels_parameter_name = (
"tick_labels"
if parse_version(matplotlib.__version__) >= parse_version("3.9")
else "labels"
)
tick_labels_dict = {tick_labels_parameter_name: feature_names[sorted_idx]}
ax.boxplot(result.importances[sorted_idx].T, vert=False, **tick_labels_dict)
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 :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.
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))
# %%
# 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 <neighbors_transformer>`.
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,
# :func:`~sklearn.metrics.pairwise.nan_euclidean_distances`, is used to find the nearest
# neighbors.
#
# Read more in the :ref:`User Guide <knnimpute>`.
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))
# %%
# 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 <minimal_cost_complexity_pruning>` 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_])
)
)
# %%
# Retrieve dataframes from OpenML
# -------------------------------
# :func:`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"]])
# %%
# 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.
#
# ..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 :func:`~sklearn.metrics.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 <roc_metrics>`.
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"))