89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
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"""
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=============================================================================
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Comparing Nearest Neighbors with and without Neighborhood Components Analysis
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=============================================================================
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An example comparing nearest neighbors classification with and without
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Neighborhood Components Analysis.
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It will plot the class decision boundaries given by a Nearest Neighbors
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classifier when using the Euclidean distance on the original features, versus
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using the Euclidean distance after the transformation learned by Neighborhood
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Components Analysis. The latter aims to find a linear transformation that
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maximises the (stochastic) nearest neighbor classification accuracy on the
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training set.
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"""
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# License: BSD 3 clause
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import ListedColormap
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.neighbors import (KNeighborsClassifier,
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NeighborhoodComponentsAnalysis)
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from sklearn.pipeline import Pipeline
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print(__doc__)
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n_neighbors = 1
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dataset = datasets.load_iris()
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X, y = dataset.data, dataset.target
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# we only take two features. We could avoid this ugly
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# slicing by using a two-dim dataset
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X = X[:, [0, 2]]
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X_train, X_test, y_train, y_test = \
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train_test_split(X, y, stratify=y, test_size=0.7, random_state=42)
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h = .01 # step size in the mesh
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# Create color maps
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cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
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cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
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names = ['KNN', 'NCA, KNN']
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classifiers = [Pipeline([('scaler', StandardScaler()),
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('knn', KNeighborsClassifier(n_neighbors=n_neighbors))
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]),
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Pipeline([('scaler', StandardScaler()),
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('nca', NeighborhoodComponentsAnalysis()),
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('knn', KNeighborsClassifier(n_neighbors=n_neighbors))
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])
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]
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x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
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y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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np.arange(y_min, y_max, h))
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for name, clf in zip(names, classifiers):
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clf.fit(X_train, y_train)
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score = clf.score(X_test, y_test)
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# Plot the decision boundary. For that, we will assign a color to each
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# point in the mesh [x_min, x_max]x[y_min, y_max].
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Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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plt.figure()
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plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=.8)
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# Plot also the training and testing points
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20)
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plt.xlim(xx.min(), xx.max())
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plt.ylim(yy.min(), yy.max())
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plt.title("{} (k = {})".format(name, n_neighbors))
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plt.text(0.9, 0.1, '{:.2f}'.format(score), size=15,
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ha='center', va='center', transform=plt.gca().transAxes)
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plt.show()
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