90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
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"""
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===============================================================================
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Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset
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===============================================================================
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A comparison for the decision boundaries generated on the iris dataset
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by Label Spreading, Self-training and SVM.
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This example demonstrates that Label Spreading and Self-training can learn
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good boundaries even when small amounts of labeled data are available.
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Note that Self-training with 100% of the data is omitted as it is functionally
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identical to training the SVC on 100% of the data.
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"""
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# Authors: Clay Woolam <clay@woolam.org>
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# Oliver Rausch <rauscho@ethz.ch>
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# License: BSD
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import datasets
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from sklearn.semi_supervised import LabelSpreading, SelfTrainingClassifier
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from sklearn.svm import SVC
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iris = datasets.load_iris()
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X = iris.data[:, :2]
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y = iris.target
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# step size in the mesh
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h = 0.02
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rng = np.random.RandomState(0)
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y_rand = rng.rand(y.shape[0])
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y_30 = np.copy(y)
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y_30[y_rand < 0.3] = -1 # set random samples to be unlabeled
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y_50 = np.copy(y)
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y_50[y_rand < 0.5] = -1
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# we create an instance of SVM and fit out data. We do not scale our
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# data since we want to plot the support vectors
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ls30 = (LabelSpreading().fit(X, y_30), y_30, "Label Spreading 30% data")
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ls50 = (LabelSpreading().fit(X, y_50), y_50, "Label Spreading 50% data")
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ls100 = (LabelSpreading().fit(X, y), y, "Label Spreading 100% data")
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# the base classifier for self-training is identical to the SVC
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base_classifier = SVC(kernel="rbf", gamma=0.5, probability=True)
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st30 = (
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SelfTrainingClassifier(base_classifier).fit(X, y_30),
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y_30,
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"Self-training 30% data",
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)
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st50 = (
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SelfTrainingClassifier(base_classifier).fit(X, y_50),
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y_50,
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"Self-training 50% data",
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)
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rbf_svc = (SVC(kernel="rbf", gamma=0.5).fit(X, y), y, "SVC with rbf kernel")
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# create a mesh to plot in
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x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
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y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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color_map = {-1: (1, 1, 1), 0: (0, 0, 0.9), 1: (1, 0, 0), 2: (0.8, 0.6, 0)}
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classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)
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for i, (clf, y_train, title) in enumerate(classifiers):
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# Plot the decision boundary. For that, we will assign a color to each
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# point in the mesh [x_min, x_max]x[y_min, y_max].
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plt.subplot(3, 2, i + 1)
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Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
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plt.axis("off")
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# Plot also the training points
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colors = [color_map[y] for y in y_train]
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plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors="black")
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plt.title(title)
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plt.suptitle("Unlabeled points are colored white", y=0.1)
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plt.show()
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