Imports for scikit-learn 1.2 Release Highlights: set_output API, Interaction Constraints, and New Displays¶
This release introduced the set_output API for pandas DataFrame output from transformers, interaction constraints in HistGradientBoosting estimators, and new visualization classes PredictionErrorDisplay and LearningCurveDisplay: The set_output(transform="pandas") method enables any transformer or pipeline to return pandas DataFrames instead of NumPy arrays, preserving column names through the entire preprocessing chain – critical for debugging and interpretability. Interaction constraints via the interaction_cst parameter restrict which features can appear together in the same tree branch, enforcing domain knowledge like “age and income should be modeled independently” by specifying groups of features that are allowed to interact.
PredictionErrorDisplay provides two complementary regression diagnostic plots (actual vs. predicted and residual vs. predicted), while LearningCurveDisplay visualizes how training and validation scores change as the training set size increases: The actual-vs-predicted plot reveals systematic bias (points far from the diagonal), while the residual plot exposes heteroscedasticity (fan-shaped residuals). PartialDependenceDisplay gained categorical_features support for bar plot and heatmap rendering of categorical partial dependence. The release also added experimental Array API support in LinearDiscriminantAnalysis for GPU-accelerated computation via libraries like CuPy, and continued the multi-version effort to optimize pairwise distance computations for float32 and sparse inputs.
# ruff: noqa: CPY001, E501
"""
=======================================
Release Highlights for scikit-learn 1.2
=======================================
.. currentmodule:: sklearn
We are pleased to announce the release of scikit-learn 1.2! Many bug fixes
and improvements were added, as well as some new key 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_1_2>`.
To install the latest version (with pip)::
pip install --upgrade scikit-learn
or with conda::
conda install -c conda-forge scikit-learn
"""
# %%
# Pandas output with `set_output` API
# -----------------------------------
# scikit-learn's transformers now support pandas output with the `set_output` API.
# To learn more about the `set_output` API see the example:
# :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` and
# # this `video, pandas DataFrame output for scikit-learn transformers
# (some examples) <https://youtu.be/5bCg8VfX2x8>`__.
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.datasets import load_iris
from sklearn.preprocessing import KBinsDiscretizer, StandardScaler
X, y = load_iris(as_frame=True, return_X_y=True)
sepal_cols = ["sepal length (cm)", "sepal width (cm)"]
petal_cols = ["petal length (cm)", "petal width (cm)"]
preprocessor = ColumnTransformer(
[
("scaler", StandardScaler(), sepal_cols),
(
"kbin",
KBinsDiscretizer(encode="ordinal", quantile_method="averaged_inverted_cdf"),
petal_cols,
),
],
verbose_feature_names_out=False,
).set_output(transform="pandas")
X_out = preprocessor.fit_transform(X)
X_out.sample(n=5, random_state=0)
# %%
# Interaction constraints in Histogram-based Gradient Boosting Trees
# ------------------------------------------------------------------
# :class:`~ensemble.HistGradientBoostingRegressor` and
# :class:`~ensemble.HistGradientBoostingClassifier` now supports interaction constraints
# with the `interaction_cst` parameter. For details, see the
# :ref:`User Guide <interaction_cst_hgbt>`. In the following example, features are not
# allowed to interact.
from sklearn.datasets import load_diabetes
from sklearn.ensemble import HistGradientBoostingRegressor
X, y = load_diabetes(return_X_y=True, as_frame=True)
hist_no_interact = HistGradientBoostingRegressor(
interaction_cst=[[i] for i in range(X.shape[1])], random_state=0
)
hist_no_interact.fit(X, y)
# %%
# New and enhanced displays
# -------------------------
# :class:`~metrics.PredictionErrorDisplay` provides a way to analyze regression
# models in a qualitative manner.
import matplotlib.pyplot as plt
from sklearn.metrics import PredictionErrorDisplay
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
_ = PredictionErrorDisplay.from_estimator(
hist_no_interact, X, y, kind="actual_vs_predicted", ax=axs[0]
)
_ = PredictionErrorDisplay.from_estimator(
hist_no_interact, X, y, kind="residual_vs_predicted", ax=axs[1]
)
# %%
# :class:`~model_selection.LearningCurveDisplay` is now available to plot
# results from :func:`~model_selection.learning_curve`.
from sklearn.model_selection import LearningCurveDisplay
_ = LearningCurveDisplay.from_estimator(
hist_no_interact, X, y, cv=5, n_jobs=2, train_sizes=np.linspace(0.1, 1, 5)
)
# %%
# :class:`~inspection.PartialDependenceDisplay` exposes a new parameter
# `categorical_features` to display partial dependence for categorical features
# using bar plots and heatmaps.
from sklearn.datasets import fetch_openml
X, y = fetch_openml(
"titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
)
X = X.select_dtypes(["number", "category"]).drop(columns=["body"])
# %%
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OrdinalEncoder
categorical_features = ["pclass", "sex", "embarked"]
model = make_pipeline(
ColumnTransformer(
transformers=[("cat", OrdinalEncoder(), categorical_features)],
remainder="passthrough",
),
HistGradientBoostingRegressor(random_state=0),
).fit(X, y)
# %%
from sklearn.inspection import PartialDependenceDisplay
fig, ax = plt.subplots(figsize=(14, 4), constrained_layout=True)
_ = PartialDependenceDisplay.from_estimator(
model,
X,
features=["age", "sex", ("pclass", "sex")],
categorical_features=categorical_features,
ax=ax,
)
# %%
# Faster parser in :func:`~datasets.fetch_openml`
# -----------------------------------------------
# :func:`~datasets.fetch_openml` now supports a new `"pandas"` parser that is
# more memory and CPU efficient. In v1.4, the default will change to
# `parser="auto"` which will automatically use the `"pandas"` parser for dense
# data and `"liac-arff"` for sparse data.
X, y = fetch_openml(
"titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
)
X.head()
# %%
# Experimental Array API support in :class:`~discriminant_analysis.LinearDiscriminantAnalysis`
# --------------------------------------------------------------------------------------------
# Experimental support for the `Array API <https://data-apis.org/array-api/latest/>`_
# specification was added to :class:`~discriminant_analysis.LinearDiscriminantAnalysis`.
# The estimator can now run on any Array API compliant libraries such as
# `CuPy <https://docs.cupy.dev/en/stable/overview.html>`__, a GPU-accelerated array
# library. For details, see the :ref:`User Guide <array_api>`.
# %%
# Improved efficiency of many estimators
# --------------------------------------
# In version 1.1 the efficiency of many estimators relying on the computation of
# pairwise distances (essentially estimators related to clustering, manifold
# learning and neighbors search algorithms) was greatly improved for float64
# dense input. Efficiency improvement especially were a reduced memory footprint
# and a much better scalability on multi-core machines.
# In version 1.2, the efficiency of these estimators was further improved for all
# combinations of dense and sparse inputs on float32 and float64 datasets, except
# the sparse-dense and dense-sparse combinations for the Euclidean and Squared
# Euclidean Distance metrics.
# A detailed list of the impacted estimators can be found in the
# :ref:`changelog <release_notes_1_2>`.