FIX Bounds in anisotropic GP hyperparameter optimization #2867
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This is exactly the same solution as in #2798 with another unit test. :) |
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That's true. I would recommend to merge #2798 in order to prevent that we get a third bug fix. |
| random_start=random_start, verbose=False) | ||
| gp.fit(X, y) | ||
| y_pred, MSE = gp.predict(X, eval_MSE=True) | ||
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| assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.)) | ||
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| assert_true(np.all(gp.theta_ >= thetaL)) # Lower bounds of hyperparameters | ||
| assert_true(np.all(gp.theta_ <= thetaU)) # Upper bounds of hyperparameters |
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PEP8:
tests/test_gaussian_process.py:73:45: E261 at least two spaces before inline comment
tests/test_gaussian_process.py:74:45: E261 at least two spaces before inline comment
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I have never even looked at this code. Linear classifiers and trees are my game :) |
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Could someone review this bugfix PR and merge it such that the issue is fixed in the 0.15 release? |
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@ogrisel This is something which you might want to have included in 0.15. |
larsmans
pushed a commit
that referenced
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Jun 25, 2014
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Merged as 9789fdb. |
Thanks for merging! |
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Currently, if multi-dimensional thetaU and thetaL are given as bounds for anisotropic GP hyperparameter optimization, only the bounds in the last dimensions are obeyed (see added unittest). This pull request fixes this bug. The bug was related to https://stackoverflow.com/questions/10452770/python-lambdas-binding-to-local-values