8000 Bugfixes in gaussian_process subpackage by dubourg · Pull Request #2632 · scikit-learn/scikit-learn · GitHub
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Bugfixes in gaussian_process subpackage #2632

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Merged
merged 2 commits into from
Aug 11, 2014
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Bugfixes in gaussian_process subpackage #2632

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fc15658
FIX: random_start feature in GaussianProcess
dubourg Dec 3, 2013
2912384
FIX: gp_diabetes_dataset examples (theta_ attribute)
dubourg Dec 3, 2013
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2 changes: 1 addition & 1 deletion examples/gaussian_process/gp_diabetes_dataset.py
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Expand Up @@ -40,7 +40,7 @@
gp.fit(X, y)

# Deactivate maximum likelihood estimation for the cross-validation loop
gp.theta0 = gp.theta # Given correlation parameter = MLE
gp.theta0 = gp.theta_ # Given correlation parameter = MLE
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why Given? if it ends with _ then it's estimated more than given no?

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The trick is I am setting gp.theta0 to the theta_ estimated from the full dataset (first call to fit), and setting thetaL and thetaU to None to force theta_ to remain the same (that's what's called the given case in the fitmethod, grep for it) during the cross-validation.

gp.thetaL, gp.thetaU = None, None # None bounds deactivate MLE

# Perform a cross-validation estimate of the coefficient of determination using
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3 changes: 1 addition & 2 deletions sklearn/gaussian_process/gaussian_process.py
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Expand Up @@ -736,9 +736,8 @@ def minus_reduced_likelihood_function(log10t):
raise ve

optimal_theta = 10. ** log10_optimal_theta
optimal_minus_rlf_value, optimal_par = \
optimal_rlf_value, optimal_par = \
self.reduced_likelihood_function(theta=optimal_theta)
optimal_rlf_value = - optimal_minus_rlf_value

# Compare the new optimizer to the best previous one
if k > 0:
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