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Comparison for decision boundary generated on iris dataset between Label Propagation and SVM.
This demonstrates Label Propagation learning a good boundary even with a small amount of labeled data.
print(__doc__) # Authors: Clay Woolam <[email protected]> # License: BSD import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn import svm from sklearn.semi_supervised import label_propagation rng = np.random.RandomState(0) iris = datasets.load_iris() X = iris.data[:, :2] y = iris.target # step size in the mesh h = .02 y_30 = np.copy(y) y_30[rng.rand(len(y)) < 0.3] = -1 y_50 = np.copy(y) y_50[rng.rand(len(y)) < 0.5] = -1 # we create an instance of SVM and fit out data. We do not scale our # data since we want to plot the support vectors ls30 = (label_propagation.LabelSpreading().fit(X, y_30), y_30) ls50 = (label_propagation.LabelSpreading().fit(X, y_50), y_50) ls100 = (label_propagation.LabelSpreading().fit(X, y), y) rbf_svc = (svm.SVC(kernel='rbf', gamma=.5).fit(X, y), y) # create a mesh to plot in 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)) # title for the plots titles = ['Label Spreading 30% data', 'Label Spreading 50% data', 'Label Spreading 100% data', 'SVC with rbf kernel'] color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)} for i, (clf, y_train) in enumerate((ls30, ls50, ls100, rbf_svc)): # 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]. plt.subplot(2, 2, i + 1) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.contourf(xx, yy, Z, cmap=plt.cm.Paired) plt.axis('off') # Plot also the training points colors = [color_map[y] for y in y_train] plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black') plt.title(titles[i]) plt.suptitle("Unlabeled points are colored white", y=0.1) plt.show()
Total running time of the script: ( 0 minutes 0.834 seconds)
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http://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_versus_svm_iris.html