.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/gaussian_process/plot_gpc_iris.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. 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_gaussian_process_plot_gpc_iris.py: ===================================================== Gaussian process classification (GPC) on iris dataset ===================================================== This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the iris-dataset. The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by assigning different length-scales to the two feature dimensions. .. GENERATED FROM PYTHON SOURCE LINES 12-63 .. image-sg:: /auto_examples/gaussian_process/images/sphx_glr_plot_gpc_iris_001.png :alt: Isotropic RBF, LML: -48.316, Anisotropic RBF, LML: -47.888 :srcset: /auto_examples/gaussian_process/images/sphx_glr_plot_gpc_iris_001.png :class: sphx-glr-single-img .. code-block:: default import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. y = np.array(iris.target, dtype=int) h = 0.02 # step size in the mesh kernel = 1.0 * RBF([1.0]) gpc_rbf_isotropic = GaussianProcessClassifier(kernel=kernel).fit(X, y) kernel = 1.0 * RBF([1.0, 1.0]) gpc_rbf_anisotropic = GaussianProcessClassifier(kernel=kernel).fit(X, 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)) titles = ["Isotropic RBF", "Anisotropic RBF"] plt.figure(figsize=(10, 5)) for i, clf in enumerate((gpc_rbf_isotropic, gpc_rbf_anisotropic)): # Plot the predicted probabilities. For that, we will assign a color to # each point in the mesh [x_min, m_max]x[y_min, y_max]. plt.subplot(1, 2, i + 1) Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape((xx.shape[0], xx.shape[1], 3)) plt.imshow(Z, extent=(x_min, x_max, y_min, y_max), origin="lower") # Plot also the training points plt.scatter(X[:, 0], X[:, 1], c=np.array(["r", "g", "b"])[y], edgecolors=(0, 0, 0)) plt.xlabel("Sepal length") plt.ylabel("Sepal width") plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.xticks(()) plt.yticks(()) plt.title( "%s, LML: %.3f" % (titles[i], clf.log_marginal_likelihood(clf.kernel_.theta)) ) plt.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 3.369 seconds) .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpc_iris.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.1.X?urlpath=lab/tree/notebooks/auto_examples/gaussian_process/plot_gpc_iris.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gpc_iris.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gpc_iris.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_