52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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========================================================================
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Gaussian Processes regression: goodness-of-fit on the 'diabetes' dataset
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========================================================================
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This example consists in fitting a Gaussian Process model onto the diabetes
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dataset.
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The correlation parameters are determined by means of maximum likelihood
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estimation (MLE). An anisotropic squared exponential correlation model with a
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constant regression model are assumed. We also used a nugget = 1e-2 in order to
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account for the (strong) noise in the targets.
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We compute then compute a cross-validation estimate of the coefficient of
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determination (R2) without reperforming MLE, using the set of correlation
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parameters found on the whole dataset.
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"""
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print __doc__
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# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
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# License: BSD style
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from sklearn import datasets
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from sklearn.gaussian_process import GaussianProcess
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from sklearn.cross_validation import cross_val_score, KFold
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# Load the dataset from scikit's data sets
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diabetes = datasets.load_diabetes()
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X, y = diabetes.data, diabetes.target
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# Instanciate a GP model
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gp = GaussianProcess(regr='constant', corr='absolute_exponential',
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theta0=[1e-4] * 10, thetaL=[1e-12] * 10,
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thetaU=[1e-2] * 10, nugget=1e-2, optimizer='Welch')
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# Fit the GP model to the data performing maximum likelihood estimation
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gp.fit(X, y)
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# Deactivate maximum likelihood estimation for the cross-validation loop
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gp.theta0 = gp.theta # Given correlation parameter = MLE
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gp.thetaL, gp.thetaU = None, None # None bounds deactivate MLE
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# Perform a cross-validation estimate of the coefficient of determination using
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# the cross_validation module using all CPUs available on the machine
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K = 20 # folds
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R2 = cross_val_score(gp, X, y=y, cv=KFold(y.size, K), n_jobs=1).mean()
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print("The %d-Folds estimate of the coefficient of determination is R2 = %s"
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% (K, R2))
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