107 lines
2.6 KiB
Python
107 lines
2.6 KiB
Python
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"""
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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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# 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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# %%
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# We generate a dataset with two concentric circles. In addition, a label
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# is associated with each sample of the dataset that is: 0 (belonging to
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# the outer circle), 1 (belonging to the inner circle), and -1 (unknown).
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# Here, all labels but two are tagged as unknown.
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import numpy as np
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from sklearn.datasets import make_circles
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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.full(n_samples, -1.0)
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labels[0] = outer
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labels[-1] = inner
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# %%
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# Plot raw data
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import matplotlib.pyplot as plt
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plt.figure(figsize=(4, 4))
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plt.scatter(
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X[labels == outer, 0],
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X[labels == outer, 1],
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color="navy",
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marker="s",
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lw=0,
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label="outer labeled",
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s=10,
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)
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plt.scatter(
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X[labels == inner, 0],
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X[labels == inner, 1],
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color="c",
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marker="s",
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lw=0,
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label="inner labeled",
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s=10,
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)
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plt.scatter(
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X[labels == -1, 0],
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X[labels == -1, 1],
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color="darkorange",
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marker=".",
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label="unlabeled",
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)
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plt.legend(scatterpoints=1, shadow=False, loc="center")
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_ = plt.title("Raw data (2 classes=outer and inner)")
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# %%
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#
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# The aim of :class:`~sklearn.semi_supervised.LabelSpreading` is to associate
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# a label to sample where the label is initially unknown.
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from sklearn.semi_supervised import LabelSpreading
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label_spread = LabelSpreading(kernel="knn", alpha=0.8)
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label_spread.fit(X, labels)
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# %%
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# Now, we can check which labels have been associated with each sample
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# when the label was unknown.
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output_labels = label_spread.transduction_
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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.figure(figsize=(4, 4))
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plt.scatter(
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X[outer_numbers, 0],
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X[outer_numbers, 1],
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color="navy",
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marker="s",
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lw=0,
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s=10,
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label="outer learned",
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)
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plt.scatter(
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X[inner_numbers, 0],
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X[inner_numbers, 1],
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color="c",
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marker="s",
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lw=0,
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s=10,
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label="inner learned",
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)
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plt.legend(scatterpoints=1, shadow=False, loc="center")
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plt.title("Labels learned with Label Spreading (KNN)")
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plt.show()
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