Nearest Neighbors ClassificationΒΆ

Sample usage of Nearest Neighbors classification. It will plot the decision boundaries for each class.

Adapted from https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html

  • ../../_images/sphx_glr_plot_classification_001.png
  • ../../_images/sphx_glr_plot_classification_002.png

Out:

/home/user/feldbauer/PycharmProjects/hubness/examples/sklearn/plot_classification.py:61: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import datasets
from skhubness.neighbors import KNeighborsClassifier

n_neighbors = 15

# import some data to play with
iris = datasets.load_iris()

# we only take the first two features. We could avoid this ugly
# slicing by using a two-dim dataset
X = iris.data[:, :2]
y = iris.target

h = .02  # step size in the mesh

# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

for hubness in [None, 'mutual_proximity']:
    # we create an instance of Neighbours Classifier and fit the data.
    clf = KNeighborsClassifier(n_neighbors,
                               hubness=hubness,
                               weights='distance')
    clf.fit(X, y)

    # 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].
    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))
    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)

    # Plot also the training 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("3-Class classification (k = %i, hubness = '%s')"
              % (n_neighbors, hubness))

plt.show()

Total running time of the script: ( 0 minutes 25.940 seconds)

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