.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/release_highlights/plot_release_highlights_1_5_0.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_5_0.py: ======================================= 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 `. To install the latest version (with pip):: pip install --upgrade scikit-learn or with conda:: conda install -c conda-forge scikit-learn .. GENERATED FROM PYTHON SOURCE LINES 25-32 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 wrapping any binary classifier and setting a custom decision threshold. .. GENERATED FROM PYTHON SOURCE LINES 32-44 .. code-block:: Python from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import ConfusionMatrixDisplay X, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) classifier_05 = LogisticRegression(C=1e6, random_state=0).fit(X_train, y_train) _ = ConfusionMatrixDisplay.from_estimator(classifier_05, X_test, y_test) .. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_5_0_001.png :alt: plot release highlights 1 5 0 :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_5_0_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 45-48 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). .. GENERATED FROM PYTHON SOURCE LINES 48-54 .. code-block:: Python from sklearn.model_selection import FixedThresholdClassifier classifier_01 = FixedThresholdClassifier(classifier_05, threshold=0.1) classifier_01.fit(X_train, y_train) _ = ConfusionMatrixDisplay.from_estimator(classifier_01, X_test, y_test) .. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_5_0_002.png :alt: plot release highlights 1 5 0 :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_5_0_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 55-75 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`. It is particularly useful to find the best decision threshold when the model is meant to be deployed in a specific application context where we can assign different gains or costs for true positives, true negatives, false positives, and false negatives. Let's illustrate this by considering an arbitrary case where: - each true positive gains 1 unit of profit, e.g. euro, year of life in good health, etc.; - true negatives gain or cost nothing; - each false negative costs 2; - each false positive costs 0.1. Our metric quantifies the average profit per sample, which is defined by the following Python function: .. GENERATED FROM PYTHON SOURCE LINES 75-86 .. code-block:: Python from sklearn.metrics import confusion_matrix def custom_score(y_observed, y_pred): tn, fp, fn, tp = confusion_matrix(y_observed, y_pred, normalize="all").ravel() return tp - 2 * fn - 0.1 * fp print("Untuned decision threshold: 0.5") print(f"Custom score: {custom_score(y_test, classifier_05.predict(X_test)):.2f}") .. rst-class:: sphx-glr-script-out .. code-block:: none Untuned decision threshold: 0.5 Custom score: -0.12 .. GENERATED FROM PYTHON SOURCE LINES 87-93 It is interesting to observe that the average gain per prediction is negative which means that this decision system is making a loss on average. Tuning the threshold to optimize this custom metric gives a smaller threshold that allows more samples to be classified as the positive class. As a result, the average gain per prediction improves. .. GENERATED FROM PYTHON SOURCE LINES 93-106 .. code-block:: Python from sklearn.model_selection import TunedThresholdClassifierCV from sklearn.metrics import make_scorer custom_scorer = make_scorer( custom_score, response_method="predict", greater_is_better=True ) tuned_classifier = TunedThresholdClassifierCV( classifier_05, cv=5, scoring=custom_scorer ).fit(X, y) print(f"Tuned decision threshold: {tuned_classifier.best_threshold_:.3f}") print(f"Custom score: {custom_score(y_test, tuned_classifier.predict(X_test)):.2f}") .. rst-class:: sphx-glr-script-out .. code-block:: none Tuned decision threshold: 0.071 Custom score: 0.04 .. GENERATED FROM PYTHON SOURCE LINES 107-121 We observe that tuning the decision threshold can turn a machine learning-based system that makes a loss on average into a beneficial one. In practice, defining a meaningful application-specific metric might involve making those costs for bad predictions and gains for good predictions depend on auxiliary metadata specific to each individual data point such as the amount of a transaction in a fraud detection system. To achieve this, :class:`~model_selection.TunedThresholdClassifierCV` leverages metadata routing support (:ref:`Metadata Routing User Guide`) allowing to optimize complex business metrics as detailed in :ref:`Post-tuning the decision threshold for cost-sensitive learning `. .. GENERATED FROM PYTHON SOURCE LINES 123-128 Performance improvements in PCA ------------------------------- :class:`~decomposition.PCA` has a new solver, `"covariance_eigh"`, which is up to an order of magnitude faster and more memory efficient than the other solvers for datasets with many data points and few features. .. GENERATED FROM PYTHON SOURCE LINES 128-139 .. code-block:: Python 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, svd_solver="covariance_eigh").fit(X) print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2f}") .. rst-class:: sphx-glr-script-out .. code-block:: none Explained variance: 0.88 .. GENERATED FROM PYTHON SOURCE LINES 140-141 The new solver also accepts sparse input data: .. GENERATED FROM PYTHON SOURCE LINES 141-148 .. code-block:: Python from scipy.sparse import random X = random(10_000, 100, format="csr", random_state=0) pca = PCA(n_components=10, svd_solver="covariance_eigh").fit(X) print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2f}") .. rst-class:: sphx-glr-script-out .. code-block:: none Explained variance: 0.13 .. GENERATED FROM PYTHON SOURCE LINES 149-157 The `"full"` solver has also been improved to use less memory and allows faster transformation. The default `svd_solver="auto"`` option takes advantage of the new solver and is now able to select an appropriate solver for sparse datasets. Similarly to most other PCA solvers, the new `"covariance_eigh"` solver can leverage GPU computation if the input data is passed as a PyTorch or CuPy array by enabling the experimental support for :ref:`Array API `. .. GENERATED FROM PYTHON SOURCE LINES 159-163 ColumnTransformer is subscriptable ---------------------------------- The transformers of a :class:`~compose.ColumnTransformer` can now be directly accessed using indexing by name. .. GENERATED FROM PYTHON SOURCE LINES 163-177 .. code-block:: Python 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"]) .. rst-class:: sphx-glr-script-out .. code-block:: none StandardScaler() OneHotEncoder() .. GENERATED FROM PYTHON SOURCE LINES 178-183 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. .. GENERATED FROM PYTHON SOURCE LINES 183-206 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none array([[-1.1, 1.1, 1.1], [ 3.9, -1.2, 1.1], [ 0.1, 1.3, 1.1], [-0.1, -1.4, -1.4], [-4.9, 1.5, -1.5], [ 0.1, 1.6, 1.6]]) .. GENERATED FROM PYTHON SOURCE LINES 207-211 Pairwise distances with non-numeric arrays ------------------------------------------ :func:`~metrics.pairwise_distances` can now compute distances between non-numeric arrays using a callable metric. .. GENERATED FROM PYTHON SOURCE LINES 211-231 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none array([[0., 3.], [3., 2.]]) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.788 seconds) .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_5_0.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/release_highlights/plot_release_highlights_1_5_0.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_release_highlights_1_5_0.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_release_highlights_1_5_0.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_release_highlights_1_5_0.zip ` .. include:: plot_release_highlights_1_5_0.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_