97 lines
2.7 KiB
Python
97 lines
2.7 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 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.inspection import DecisionBoundaryDisplay
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from sklearn.model_selection import train_test_split
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from sklearn.neighbors import KNeighborsClassifier, NeighborhoodComponentsAnalysis
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import StandardScaler
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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 = train_test_split(
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X, y, stratify=y, test_size=0.7, random_state=42
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)
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h = 0.05 # 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 = [
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Pipeline(
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[
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("scaler", StandardScaler()),
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("knn", KNeighborsClassifier(n_neighbors=n_neighbors)),
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]
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),
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Pipeline(
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[
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("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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]
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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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_, ax = plt.subplots()
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DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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cmap=cmap_light,
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alpha=0.8,
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ax=ax,
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response_method="predict",
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plot_method="pcolormesh",
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shading="auto",
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)
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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.title("{} (k = {})".format(name, n_neighbors))
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plt.text(
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0.9,
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0.1,
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"{:.2f}".format(score),
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size=15,
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ha="center",
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va="center",
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transform=plt.gca().transAxes,
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)
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plt.show()
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