.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/neighbors/plot_nca_classification.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_neighbors_plot_nca_classification.py: ============================================================================= Comparing Nearest Neighbors with and without Neighborhood Components Analysis ============================================================================= An example comparing nearest neighbors classification with and without Neighborhood Components Analysis. It will plot the class decision boundaries given by a Nearest Neighbors classifier when using the Euclidean distance on the original features, versus using the Euclidean distance after the transformation learned by Neighborhood Components Analysis. The latter aims to find a linear transformation that maximises the (stochastic) nearest neighbor classification accuracy on the training set. .. GENERATED FROM PYTHON SOURCE LINES 17-98 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_001.png :alt: KNN (k = 1) :srcset: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_002.png :alt: NCA, KNN (k = 1) :srcset: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_002.png :class: sphx-glr-multi-img .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn import datasets from sklearn.inspection import DecisionBoundaryDisplay from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier, NeighborhoodComponentsAnalysis from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler n_neighbors = 1 dataset = datasets.load_iris() X, y = dataset.data, dataset.target # we only take two features. We could avoid this ugly # slicing by using a two-dim dataset X = X[:, [0, 2]] X_train, X_test, y_train, y_test = train_test_split( X, y, stratify=y, test_size=0.7, random_state=42 ) h = 0.05 # step size in the mesh # Create color maps cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"]) cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"]) names = ["KNN", "NCA, KNN"] classifiers = [ Pipeline( [ ("scaler", StandardScaler()), ("knn", KNeighborsClassifier(n_neighbors=n_neighbors)), ] ), Pipeline( [ ("scaler", StandardScaler()), ("nca", NeighborhoodComponentsAnalysis()), ("knn", KNeighborsClassifier(n_neighbors=n_neighbors)), ] ), ] for name, clf in zip(names, classifiers): clf.fit(X_train, y_train) score = clf.score(X_test, y_test) _, ax = plt.subplots() DecisionBoundaryDisplay.from_estimator( clf, X, cmap=cmap_light, alpha=0.8, ax=ax, response_method="predict", plot_method="pcolormesh", shading="auto", ) # Plot also the training and testing points plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor="k", s=20) plt.title("{} (k = {})".format(name, n_neighbors)) plt.text( 0.9, 0.1, "{:.2f}".format(score), size=15, ha="center", va="center", transform=plt.gca().transAxes, ) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.789 seconds) .. _sphx_glr_download_auto_examples_neighbors_plot_nca_classification.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/main?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_nca_classification.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/neighbors/plot_nca_classification.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nca_classification.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nca_classification.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_nca_classification.zip ` .. include:: plot_nca_classification.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_