DOC Release highlights 1.5 (#29007)

jeremiedbb · betatim · glemaitre · jeremiedbb · commit 0ac28ade871c · 2024-05-21T15:57:58.000+02:00
Co-authored-by: Tim Head &lt;betatim@gmail.com&gt;
Co-authored-by: Guillaume Lemaitre &lt;guillaume@probabl.ai&gt;
Co-authored-by: Christian Lorentzen &lt;lorentzen.ch@gmail.com&gt;
diff --git a/examples/release_highlights/plot_release_highlights_1_5_0.py b/examples/release_highlights/plot_release_highlights_1_5_0.py
@@ -0,0 +1,183 @@
+# ruff: noqa
+"""
+=======================================
+Release Highlights for scikit-learn 1.5
+=======================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 1.5! Many bug fixes
+and improvements were added, as well as some key new features. Below we
+detail the highlights of this release. **For an exhaustive list of
+all the changes**, please refer to the :ref:`release notes <release_notes_1_5>`.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+
+"""
+
+# %%
+# FixedThresholdClassifier: Setting the decision threshold of a binary classifier
+# -------------------------------------------------------------------------------
+# All binary classifiers of scikit-learn use a fixed decision threshold of 0.5 to
+# convert probability estimates (i.e. output of `predict_proba`) into class
+# predictions. However, 0.5 is almost never the desired threshold for a given problem.
+# :class:`~model_selection.FixedThresholdClassifier` allows to wrap any binary
+# classifier and set a custom decision threshold.
+from sklearn.datasets import make_classification
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import confusion_matrix
+
+X, y = make_classification(n_samples=1_000, weights=[0.9, 0.1], random_state=0)
+classifier = LogisticRegression(random_state=0).fit(X, y)
+
+print("confusion matrix:\n", confusion_matrix(y, classifier.predict(X)))
+
+# %%
+# Lowering the threshold, i.e. allowing more samples to be classified as the positive
+# class, increases the number of true positives at the cost of more false positives
+# (as is well known from the concavity of the ROC curve).
+from sklearn.model_selection import FixedThresholdClassifier
+
+wrapped_classifier = FixedThresholdClassifier(classifier, threshold=0.1).fit(X, y)
+
+print("confusion matrix:\n", confusion_matrix(y, wrapped_classifier.predict(X)))
+
+# %%
+# TunedThresholdClassifierCV: Tuning the decision threshold of a binary classifier
+# --------------------------------------------------------------------------------
+# The decision threshold of a binary classifier can be tuned to optimize a given
+# metric, using :class:`~model_selection.TunedThresholdClassifierCV`.
+from sklearn.metrics import balanced_accuracy_score
+
+# Due to the class imbalance, the balanced accuracy is not optimal for the default
+# threshold. The classifier tends to over predict the majority class.
+print(f"balanced accuracy: {balanced_accuracy_score(y, classifier.predict(X)):.2f}")
+
+# %%
+# Tuning the threshold to optimize the balanced accuracy gives a smaller threshold
+# that allows more samples to be classified as the positive class.
+from sklearn.model_selection import TunedThresholdClassifierCV
+
+tuned_classifier = TunedThresholdClassifierCV(
+    classifier, cv=5, scoring="balanced_accuracy"
+).fit(X, y)
+
+print(f"new threshold: {tuned_classifier.best_threshold_:.4f}")
+print(
+    f"balanced accuracy: {balanced_accuracy_score(y, tuned_classifier.predict(X)):.2f}"
+)
+
+# %%
+# :class:`~model_selection.TunedThresholdClassifierCV` also benefits from the
+# metadata routing support (:ref:`Metadata Routing User Guide<metadata_routing>`)
+# allowing to optimze complex business metrics, detailed
+# in :ref:`Post-tuning the decision threshold for cost-sensitive learning
+# <sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py>`.
+
+# %%
+# Performance improvements in PCA
+# -------------------------------
+# :class:`~decomposition.PCA` has a new solver, "covariance_eigh", which is faster
+# and more memory efficient than the other solvers for datasets with a large number
+# of samples and a small number of features.
+from sklearn.datasets import make_low_rank_matrix
+from sklearn.decomposition import PCA
+
+X = make_low_rank_matrix(
+    n_samples=10_000, n_features=100, tail_strength=0.1, random_state=0
+)
+
+pca = PCA(n_components=10).fit(X)
+
+print(f"explained variance: {pca.explained_variance_ratio_.sum():.2f}")
+
+# %%
+# The "full" solver has also been improved to use less memory and allows to
+# transform faster. The "auto" option for the solver takes advantage of the
+# new solver and is now able to select an appropriate solver for sparse
+# datasets.
+from scipy.sparse import random
+
+X = random(10000, 100, format="csr", random_state=0)
+
+pca = PCA(n_components=10, svd_solver="auto").fit(X)
+
+# %%
+# ColumnTransformer is subscriptable
+# ----------------------------------
+# The transformers of a :class:`~compose.ColumnTransformer` can now be directly
+# accessed using indexing by name.
+import numpy as np
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+
+X = np.array([[0, 1, 2], [3, 4, 5]])
+column_transformer = ColumnTransformer(
+    [("std_scaler", StandardScaler(), [0]), ("one_hot", OneHotEncoder(), [1, 2])]
+)
+
+column_transformer.fit(X)
+
+print(column_transformer["std_scaler"])
+print(column_transformer["one_hot"])
+
+# %%
+# Custom imputation strategies for the SimpleImputer
+# --------------------------------------------------
+# :class:`~impute.SimpleImputer` now supports custom strategies for imputation,
+# using a callable that computes a scalar value from the non missing values of
+# a column vector.
+from sklearn.impute import SimpleImputer
+
+X = np.array(
+    [
+        [-1.1, 1.1, 1.1],
+        [3.9, -1.2, np.nan],
+        [np.nan, 1.3, np.nan],
+        [-0.1, -1.4, -1.4],
+        [-4.9, 1.5, -1.5],
+        [np.nan, 1.6, 1.6],
+    ]
+)
+
+
+def smallest_abs(arr):
+    """Return the smallest absolute value of a 1D array."""
+    return np.min(np.abs(arr))
+
+
+imputer = SimpleImputer(strategy=smallest_abs)
+
+imputer.fit_transform(X)
+
+# %%
+# Pairwise distances with non-numeric arrays
+# ------------------------------------------
+# :func:`~metrics.pairwise_distances` can now compute distances between
+# non-numeric arrays using a callable metric.
+from sklearn.metrics import pairwise_distances
+
+X = ["cat", "dog"]
+Y = ["cat", "fox"]
+
+
+def levenshtein_distance(x, y):
+    """Return the Levenshtein distance between two strings."""
+    if x == "" or y == "":
+        return max(len(x), len(y))
+    if x[0] == y[0]:
+        return levenshtein_distance(x[1:], y[1:])
+    return 1 + min(
+        levenshtein_distance(x[1:], y),
+        levenshtein_distance(x, y[1:]),
+        levenshtein_distance(x[1:], y[1:]),
+    )
+
+
+pairwise_distances(X, Y, metric=levenshtein_distance)
