.. 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 Click :ref:`here ` to download the full example code or to run this example in your browser via 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 16-89 .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_001.png :alt: KNN (k = 1) :class: sphx-glr-multi-img * .. image:: /auto_examples/neighbors/images/sphx_glr_plot_nca_classification_002.png :alt: NCA, KNN (k = 1) :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/circleci/project/examples/neighbors/plot_nca_classification.py:78: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later. plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=.8) /home/circleci/project/examples/neighbors/plot_nca_classification.py:78: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later. plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=.8) | .. code-block:: default # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neighbors import (KNeighborsClassifier, NeighborhoodComponentsAnalysis) from sklearn.pipeline import Pipeline print(__doc__) 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 = .01 # 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)) ]) ] x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) for name, clf in zip(names, classifiers): clf.fit(X_train, y_train) score = clf.score(X_test, y_test) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, x_max]x[y_min, y_max]. Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure() plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=.8) # Plot also the training and testing points plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20) plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) 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 21.798 seconds) .. _sphx_glr_download_auto_examples_neighbors_plot_nca_classification.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_nca_classification.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nca_classification.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nca_classification.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_