Note
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Example of LabelPropagation learning a complex internal structure to demonstrate “manifold learning”. The outer circle should be labeled “red” and the inner circle “blue”. Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle.
print(__doc__) # Authors: Clay Woolam <[email protected]> # Andreas Mueller <[email protected]> # License: BSD import numpy as np import matplotlib.pyplot as plt from sklearn.semi_supervised import label_propagation from sklearn.datasets import make_circles # generate ring with inner box n_samples = 200 X, y = make_circles(n_samples=n_samples, shuffle=False) outer, inner = 0, 1 labels = np.full(n_samples, -1.) labels[0] = outer labels[-1] = inner # ############################################################################# # Learn with LabelSpreading label_spread = label_propagation.LabelSpreading(kernel='knn', alpha=0.8) label_spread.fit(X, labels) # ############################################################################# # Plot output labels output_labels = label_spread.transduction_ plt.figure(figsize=(8.5, 4)) plt.subplot(1, 2, 1) plt.scatter(X[labels == outer, 0], X[labels == outer, 1], color='navy', marker='s', lw=0, label="outer labeled", s=10) plt.scatter(X[labels == inner, 0], X[labels == inner, 1], color='c', marker='s', lw=0, label='inner labeled', s=10) plt.scatter(X[labels == -1, 0], X[labels == -1, 1], color='darkorange', marker='.', label='unlabeled') plt.legend(scatterpoints=1, shadow=False, loc='upper right') plt.title("Raw data (2 classes=outer and inner)") plt.subplot(1, 2, 2) output_label_array = np.asarray(output_labels) outer_numbers = np.where(output_label_array == outer)[0] inner_numbers = np.where(output_label_array == inner)[0] plt.scatter(X[outer_numbers, 0], X[outer_numbers, 1], color='navy', marker='s', lw=0, s=10, label="outer learned") plt.scatter(X[inner_numbers, 0], X[inner_numbers, 1], color='c', marker='s', lw=0, s=10, label="inner learned") plt.legend(scatterpoints=1, shadow=False, loc='upper right') plt.title("Labels learned with Label Spreading (KNN)") plt.subplots_adjust(left=0.07, bottom=0.07, right=0.93, top=0.92) plt.show()
Total running time of the script: ( 0 minutes 0.046 seconds)
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http://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_structure.html