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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.

Isotropic RBF, LML: -48.316, Anisotropic RBF, LML: -47.888

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.pyplotasplt
importnumpyasnp
fromsklearnimport datasets
fromsklearn.gaussian_processimport GaussianProcessClassifier
fromsklearn.gaussian_process.kernelsimport 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 ()