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