64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
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"""
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=====================================================
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Gaussian process classification (GPC) on iris dataset
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=====================================================
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This example illustrates the predicted probability of GPC for an isotropic
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and anisotropic RBF kernel on a two-dimensional version for the iris-dataset.
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The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by
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assigning different length-scales to the two feature dimensions.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import datasets
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from sklearn.gaussian_process import GaussianProcessClassifier
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from sklearn.gaussian_process.kernels import RBF
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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features.
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y = np.array(iris.target, dtype=int)
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h = 0.02 # step size in the mesh
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kernel = 1.0 * RBF([1.0])
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gpc_rbf_isotropic = GaussianProcessClassifier(kernel=kernel).fit(X, y)
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kernel = 1.0 * RBF([1.0, 1.0])
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gpc_rbf_anisotropic = GaussianProcessClassifier(kernel=kernel).fit(X, y)
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# create a mesh to plot in
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x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
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y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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titles = ["Isotropic RBF", "Anisotropic RBF"]
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plt.figure(figsize=(10, 5))
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for i, clf in enumerate((gpc_rbf_isotropic, gpc_rbf_anisotropic)):
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# Plot the predicted probabilities. For that, we will assign a color to
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# each point in the mesh [x_min, m_max]x[y_min, y_max].
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plt.subplot(1, 2, i + 1)
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Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])
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# Put the result into a color plot
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Z = Z.reshape((xx.shape[0], xx.shape[1], 3))
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plt.imshow(Z, extent=(x_min, x_max, y_min, y_max), origin="lower")
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=np.array(["r", "g", "b"])[y], edgecolors=(0, 0, 0))
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plt.xlabel("Sepal length")
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plt.ylabel("Sepal width")
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plt.xlim(xx.min(), xx.max())
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plt.ylim(yy.min(), yy.max())
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plt.xticks(())
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plt.yticks(())
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plt.title(
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"%s, LML: %.3f" % (titles[i], clf.log_marginal_likelihood(clf.kernel_.theta))
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)
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plt.tight_layout()
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plt.show()
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