.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py: =============================================================================== Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset =============================================================================== A comparison for the decision boundaries generated on the iris dataset by Label Spreading, Self-training and SVM. This example demonstrates that Label Spreading and Self-training can learn good boundaries even when small amounts of labeled data are available. Note that Self-training with 100% of the data is omitted as it is functionally identical to training the SVC on 100% of the data. .. GENERATED FROM PYTHON SOURCE LINES 16-89 .. image-sg:: /auto_examples/semi_supervised/images/sphx_glr_plot_semi_supervised_versus_svm_iris_001.png :alt: Unlabeled points are colored white, Label Spreading 30% data, Self-training 30% data, Label Spreading 50% data, Self-training 50% data, Label Spreading 100% data, SVC with rbf kernel :srcset: /auto_examples/semi_supervised/images/sphx_glr_plot_semi_supervised_versus_svm_iris_001.png :class: sphx-glr-single-img .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import matplotlib.pyplot as plt import numpy as np from sklearn import datasets from sklearn.semi_supervised import LabelSpreading, SelfTrainingClassifier from sklearn.svm import SVC iris = datasets.load_iris() X = iris.data[:, :2] y = iris.target # step size in the mesh h = 0.02 rng = np.random.RandomState(0) y_rand = rng.rand(y.shape[0]) y_30 = np.copy(y) y_30[y_rand < 0.3] = -1 # set random samples to be unlabeled y_50 = np.copy(y) y_50[y_rand < 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, "Label Spreading 30% data") ls50 = (LabelSpreading().fit(X, y_50), y_50, "Label Spreading 50% data") ls100 = (LabelSpreading().fit(X, y), y, "Label Spreading 100% data") # the base classifier for self-training is identical to the SVC base_classifier = SVC(kernel="rbf", gamma=0.5, probability=True) st30 = ( SelfTrainingClassifier(base_classifier).fit(X, y_30), y_30, "Self-training 30% data", ) st50 = ( SelfTrainingClassifier(base_classifier).fit(X, y_50), y_50, "Self-training 50% data", ) rbf_svc = (SVC(kernel="rbf", gamma=0.5).fit(X, y), y, "SVC with rbf kernel") # 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)) color_map = {-1: (1, 1, 1), 0: (0, 0, 0.9), 1: (1, 0, 0), 2: (0.8, 0.6, 0)} classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc) for i, (clf, y_train, title) in enumerate(classifiers): # 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(3, 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(title) 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 0.928 seconds) .. _sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_semi_supervised_versus_svm_iris.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_semi_supervised_versus_svm_iris.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_semi_supervised_versus_svm_iris.zip ` .. include:: plot_semi_supervised_versus_svm_iris.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_