.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_semi_supervised_plot_label_propagation_versus_svm_iris.py: ===================================================================== Decision boundary of label propagation versus SVM on the Iris dataset ===================================================================== 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. .. image:: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_versus_svm_iris_001.png :class: sphx-glr-single-img .. code-block:: default print(__doc__) # Authors: Clay Woolam # License: BSD import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn import svm from sklearn.semi_supervised import LabelSpreading 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 = (LabelSpreading().fit(X, y_30), y_30) ls50 = (LabelSpreading().fit(X, y_50), y_50) ls100 = (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() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.188 seconds) **Estimated memory usage:** 79 MB .. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_versus_svm_iris.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/semi_supervised/plot_label_propagation_versus_svm_iris.ipynb :width: 150 px .. container:: sphx-glr-download :download:`Download Python source code: plot_label_propagation_versus_svm_iris.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_label_propagation_versus_svm_iris.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_