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# ruff: noqa
"""
=======================================
Release Highlights for scikit-learn 1.3
=======================================
.. currentmodule:: sklearn
We are pleased to announce the release of scikit-learn 1.3! 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_3>`.
To install the latest version (with pip)::
pip install --upgrade scikit-learn
or with conda::
conda install -c conda-forge scikit-learn
"""
# %%
# Metadata Routing
# ----------------
# We are in the process of introducing a new way to route metadata such as
# ``sample_weight`` throughout the codebase, which would affect how
# meta-estimators such as :class:`pipeline.Pipeline` and
# :class:`model_selection.GridSearchCV` route metadata. While the
# infrastructure for this feature is already included in this release, the work
# is ongoing and not all meta-estimators support this new feature. You can read
# more about this feature in the :ref:`Metadata Routing User Guide
# <metadata_routing>`. Note that this feature is still under development and
# not implemented for most meta-estimators.
#
# Third party developers can already start incorporating this into their
# meta-estimators. For more details, see
# :ref:`metadata routing developer guide
# <sphx_glr_auto_examples_miscellaneous_plot_metadata_routing.py>`.
# %%
# HDBSCAN: hierarchical density-based clustering
# ----------------------------------------------
# Originally hosted in the scikit-learn-contrib repository, :class:`cluster.HDBSCAN`
# has been adpoted into scikit-learn. It's missing a few features from the original
# implementation which will be added in future releases.
# By performing a modified version of :class:`cluster.DBSCAN` over multiple epsilon
# values simultaneously, :class:`cluster.HDBSCAN` finds clusters of varying densities
# making it more robust to parameter selection than :class:`cluster.DBSCAN`.
# More details in the :ref:`User Guide <hdbscan>`.
import numpy as np
from sklearn.cluster import HDBSCAN
from sklearn.datasets import load_digits
from sklearn.metrics import v_measure_score
X, true_labels = load_digits(return_X_y=True)
print(f"number of digits: {len(np.unique(true_labels))}")
hdbscan = HDBSCAN(min_cluster_size=15).fit(X)
non_noisy_labels = hdbscan.labels_[hdbscan.labels_ != -1]
print(f"number of clusters found: {len(np.unique(non_noisy_labels))}")
print(v_measure_score(true_labels[hdbscan.labels_ != -1], non_noisy_labels))
# %%
# TargetEncoder: a new category encoding strategy
# -----------------------------------------------
# Well suited for categorical features with high cardinality,
# :class:`preprocessing.TargetEncoder` encodes the categories based on a shrunk
# estimate of the average target values for observations belonging to that category.
# More details in the :ref:`User Guide <target_encoder>`.
import numpy as np
from sklearn.preprocessing import TargetEncoder
X = np.array([["cat"] * 30 + ["dog"] * 20 + ["snake"] * 38], dtype=object).T
y = [90.3] * 30 + [20.4] * 20 + [21.2] * 38
enc = TargetEncoder(random_state=0)
X_trans = enc.fit_transform(X, y)
enc.encodings_
# %%
# Missing values support in decision trees
# ----------------------------------------
# The classes :class:`tree.DecisionTreeClassifier` and
# :class:`tree.DecisionTreeRegressor` now support missing values. For each potential
# threshold on the non-missing data, the splitter will evaluate the split with all the
# missing values going to the left node or the right node.
# More details in the :ref:`User Guide <tree_missing_value_support>`.
import numpy as np
from sklearn.tree import DecisionTreeClassifier
X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
y = [0, 0, 1, 1]
tree = DecisionTreeClassifier(random_state=0).fit(X, y)
tree.predict(X)
# %%
# New display `model_selection.ValidationCurveDisplay`
# ----------------------------------------------------
# :class:`model_selection.ValidationCurveDisplay` is now available to plot results
# from :func:`model_selection.validation_curve`.
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import ValidationCurveDisplay
X, y = make_classification(1000, 10, random_state=0)
_ = ValidationCurveDisplay.from_estimator(
LogisticRegression(),
X,
y,
param_name="C",
param_range=np.geomspace(1e-5, 1e3, num=9),
score_type="both",
score_name="Accuracy",
)
# %%
# Gamma loss for gradient boosting
# --------------------------------
# The class :class:`ensemble.HistGradientBoostingRegressor` supports the
# Gamma deviance loss function via `loss="gamma"`. This loss function is useful for
# modeling strictly positive targets with a right-skewed distribution.
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.datasets import make_low_rank_matrix
from sklearn.ensemble import HistGradientBoostingRegressor
n_samples, n_features = 500, 10
rng = np.random.RandomState(0)
X = make_low_rank_matrix(n_samples, n_features, random_state=rng)
coef = rng.uniform(low=-10, high=20, size=n_features)
y = rng.gamma(shape=2, scale=np.exp(X @ coef) / 2)
gbdt = HistGradientBoostingRegressor(loss="gamma")
cross_val_score(gbdt, X, y).mean()
# %%
# Grouping infrequent categories in :class:`preprocessing.OrdinalEncoder`
# -----------------------------------------------------------------------
# Similarly to :class:`preprocessing.OneHotEncoder`, the class
# :class:`preprocessing.OrdinalEncoder` now supports aggregating infrequent categories
# into a single output for each feature. The parameters to enable the gathering of
# infrequent categories are `min_frequency` and `max_categories`.
# See the :ref:`User Guide <encoder_infrequent_categories>` for more details.
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
X = np.array(
[["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object
).T
enc = OrdinalEncoder(min_frequency=6).fit(X)
enc.infrequent_categories_
|
