Imports for scikit-learn 0.24 Release Highlights: Successive Halving, Self-Training, and ICE Plots¶
This release introduced HalvingRandomSearchCV and HalvingGridSearchCV for efficient hyperparameter tuning using successive halving, where candidate configurations are progressively filtered by allocating increasing amounts of resources (training samples or iterations) to the most promising candidates: Unlike exhaustive grid search which evaluates every combination on the full dataset, successive halving starts by evaluating all candidates on a small subset, discards the worst performers, and doubles the resources for survivors – reducing total computation from O(n * k) to O(n * log(k)) where k is the number of candidates.
Additional highlights include SelfTrainingClassifier for semi-supervised learning, SequentialFeatureSelector for greedy forward/backward feature selection, PolynomialCountSketch for scalable polynomial kernel approximation, Individual Conditional Expectation (ICE) plots, native categorical feature support in HistGradientBoosting, and the Poisson splitting criterion for DecisionTreeRegressor: SelfTrainingClassifier wraps any classifier with predict_proba and iteratively labels unlabeled samples (marked with -1) that exceed a confidence threshold, progressively expanding the training set. SequentialFeatureSelector greedily adds (forward) or removes (backward) features one at a time based on cross-validated score, providing a model-agnostic alternative to embedded methods like Lasso regularization.
# ruff: noqa: CPY001, E501
"""
========================================
Release Highlights for scikit-learn 0.24
========================================
.. currentmodule:: sklearn
We are pleased to announce the release of scikit-learn 0.24! 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_0_24>`.
To install the latest version (with pip)::
pip install --upgrade scikit-learn
or with conda::
conda install -c conda-forge scikit-learn
"""
##############################################################################
# Successive Halving estimators for tuning hyper-parameters
# ---------------------------------------------------------
# Successive Halving, a state of the art method, is now available to
# explore the space of the parameters and identify their best combination.
# :class:`~sklearn.model_selection.HalvingGridSearchCV` and
# :class:`~sklearn.model_selection.HalvingRandomSearchCV` can be
# used as drop-in replacement for
# :class:`~sklearn.model_selection.GridSearchCV` and
# :class:`~sklearn.model_selection.RandomizedSearchCV`.
# Successive Halving is an iterative selection process illustrated in the
# figure below. The first iteration is run with a small amount of resources,
# where the resource typically corresponds to the number of training samples,
# but can also be an arbitrary integer parameter such as `n_estimators` in a
# random forest. Only a subset of the parameter candidates are selected for the
# next iteration, which will be run with an increasing amount of allocated
# resources. Only a subset of candidates will last until the end of the
# iteration process, and the best parameter candidate is the one that has the
# highest score on the last iteration.
#
# Read more in the :ref:`User Guide <successive_halving_user_guide>` (note:
# the Successive Halving estimators are still :term:`experimental
# <experimental>`).
#
# .. figure:: ../model_selection/images/sphx_glr_plot_successive_halving_iterations_001.png
# :target: ../model_selection/plot_successive_halving_iterations.html
# :align: center
import numpy as np
from scipy.stats import randint
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.experimental import enable_halving_search_cv # noqa: F401
from sklearn.model_selection import HalvingRandomSearchCV
rng = np.random.RandomState(0)
X, y = make_classification(n_samples=700, random_state=rng)
clf = RandomForestClassifier(n_estimators=10, random_state=rng)
param_dist = {
"max_depth": [3, None],
"max_features": randint(1, 11),
"min_samples_split": randint(2, 11),
"bootstrap": [True, False],
"criterion": ["gini", "entropy"],
}
rsh = HalvingRandomSearchCV(
estimator=clf, param_distributions=param_dist, factor=2, random_state=rng
)
rsh.fit(X, y)
rsh.best_params_
##############################################################################
# Native support for categorical features in HistGradientBoosting estimators
# --------------------------------------------------------------------------
# :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and
# :class:`~sklearn.ensemble.HistGradientBoostingRegressor` now have native
# support for categorical features: they can consider splits on non-ordered,
# categorical data. Read more in the :ref:`User Guide
# <categorical_support_gbdt>`.
#
# .. figure:: ../ensemble/images/sphx_glr_plot_gradient_boosting_categorical_001.png
# :target: ../ensemble/plot_gradient_boosting_categorical.html
# :align: center
#
# The plot shows that the new native support for categorical features leads to
# fitting times that are comparable to models where the categories are treated
# as ordered quantities, i.e. simply ordinal-encoded. Native support is also
# more expressive than both one-hot encoding and ordinal encoding. However, to
# use the new `categorical_features` parameter, it is still required to
# preprocess the data within a pipeline as demonstrated in this :ref:`example
# <sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py>`.
##############################################################################
# Improved performances of HistGradientBoosting estimators
# --------------------------------------------------------
# The memory footprint of :class:`ensemble.HistGradientBoostingRegressor` and
# :class:`ensemble.HistGradientBoostingClassifier` has been significantly
# improved during calls to `fit`. In addition, histogram initialization is now
# done in parallel which results in slight speed improvements.
# See more in the `Benchmark page
# <https://scikit-learn.org/scikit-learn-benchmarks/>`_.
##############################################################################
# New self-training meta-estimator
# --------------------------------
# A new self-training implementation, based on `Yarowski's algorithm
# <https://doi.org/10.3115/981658.981684>`_ can now be used with any
# classifier that implements :term:`predict_proba`. The sub-classifier
# will behave as a
# semi-supervised classifier, allowing it to learn from unlabeled data.
# Read more in the :ref:`User guide <self_training>`.
import numpy as np
from sklearn import datasets
from sklearn.semi_supervised import SelfTrainingClassifier
from sklearn.svm import SVC
rng = np.random.RandomState(42)
iris = datasets.load_iris()
random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3
iris.target[random_unlabeled_points] = -1
svc = SVC(probability=True, gamma="auto")
self_training_model = SelfTrainingClassifier(svc)
self_training_model.fit(iris.data, iris.target)
##############################################################################
# New SequentialFeatureSelector transformer
# -----------------------------------------
# A new iterative transformer to select features is available:
# :class:`~sklearn.feature_selection.SequentialFeatureSelector`.
# Sequential Feature Selection can add features one at a time (forward
# selection) or remove features from the list of the available features
# (backward selection), based on a cross-validated score maximization.
# See the :ref:`User Guide <sequential_feature_selection>`.
from sklearn.datasets import load_iris
from sklearn.feature_selection import SequentialFeatureSelector
from sklearn.neighbors import KNeighborsClassifier
X, y = load_iris(return_X_y=True, as_frame=True)
feature_names = X.columns
knn = KNeighborsClassifier(n_neighbors=3)
sfs = SequentialFeatureSelector(knn, n_features_to_select=2)
sfs.fit(X, y)
print(
"Features selected by forward sequential selection: "
f"{feature_names[sfs.get_support()].tolist()}"
)
##############################################################################
# New PolynomialCountSketch kernel approximation function
# -------------------------------------------------------
# The new :class:`~sklearn.kernel_approximation.PolynomialCountSketch`
# approximates a polynomial expansion of a feature space when used with linear
# models, but uses much less memory than
# :class:`~sklearn.preprocessing.PolynomialFeatures`.
from sklearn.datasets import fetch_covtype
from sklearn.kernel_approximation import PolynomialCountSketch
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler
X, y = fetch_covtype(return_X_y=True)
pipe = make_pipeline(
MinMaxScaler(),
PolynomialCountSketch(degree=2, n_components=300),
LogisticRegression(max_iter=1000),
)
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=5000, test_size=10000, random_state=42
)
pipe.fit(X_train, y_train).score(X_test, y_test)
##############################################################################
# For comparison, here is the score of a linear baseline for the same data:
linear_baseline = make_pipeline(MinMaxScaler(), LogisticRegression(max_iter=1000))
linear_baseline.fit(X_train, y_train).score(X_test, y_test)
##############################################################################
# Individual Conditional Expectation plots
# ----------------------------------------
# A new kind of partial dependence plot is available: the Individual
# Conditional Expectation (ICE) plot. ICE plots visualize the dependence of the
# prediction on a feature for each sample separately, with one line per sample.
# See the :ref:`User Guide <individual_conditional>`
from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestRegressor
# from sklearn.inspection import plot_partial_dependence
from sklearn.inspection import PartialDependenceDisplay
X, y = fetch_california_housing(return_X_y=True, as_frame=True)
features = ["MedInc", "AveOccup", "HouseAge", "AveRooms"]
est = RandomForestRegressor(n_estimators=10)
est.fit(X, y)
# plot_partial_dependence has been removed in version 1.2. From 1.2, use
# PartialDependenceDisplay instead.
# display = plot_partial_dependence(
display = PartialDependenceDisplay.from_estimator(
est,
X,
features,
kind="individual",
subsample=50,
n_jobs=3,
grid_resolution=20,
random_state=0,
)
display.figure_.suptitle(
"Partial dependence of house value on non-location features\n"
"for the California housing dataset, with BayesianRidge"
)
display.figure_.subplots_adjust(hspace=0.3)
##############################################################################
# New Poisson splitting criterion for DecisionTreeRegressor
# ---------------------------------------------------------
# The integration of Poisson regression estimation continues from version 0.23.
# :class:`~sklearn.tree.DecisionTreeRegressor` now supports a new `'poisson'`
# splitting criterion. Setting `criterion="poisson"` might be a good choice
# if your target is a count or a frequency.
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
n_samples, n_features = 1000, 20
rng = np.random.RandomState(0)
X = rng.randn(n_samples, n_features)
# positive integer target correlated with X[:, 5] with many zeros:
y = rng.poisson(lam=np.exp(X[:, 5]) / 2)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
regressor = DecisionTreeRegressor(criterion="poisson", random_state=0)
regressor.fit(X_train, y_train)
##############################################################################
# New documentation improvements
# ------------------------------
#
# New examples and documentation pages have been added, in a continuous effort
# to improve the understanding of machine learning practices:
#
# - a new section about :ref:`common pitfalls and recommended
# practices <common_pitfalls>`,
# - an example illustrating how to :ref:`statistically compare the performance of
# models <sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py>`
# evaluated using :class:`~sklearn.model_selection.GridSearchCV`,
# - an example on how to :ref:`interpret coefficients of linear models
# <sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py>`,
# - an :ref:`example
# <sphx_glr_auto_examples_cross_decomposition_plot_pcr_vs_pls.py>`
# comparing Principal Component Regression and Partial Least Squares.