Comments (5)
200seconds to install conda
, do we really need a conda env?
The bulk of the time is spent in actual tests, can we simplify them and test small parts instead of full blown optimisations??
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The easiest solution is simply to reduce maxiter
. We can show that it works in much fewer iterations.
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I don't really understand how but I brought it down to 6 minutes with this commit. MechCoder@395f9dd
from scikit-optimize.
What also takes some time is compiling scikit-learn master. Hopefully, this will be solved after the next release. In #12, tests are now down to 5mn total.
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Closing this, tests are now fine.
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Related Issues (20)
- BayesSearchCV: default value mentioned for cv is wrong in the doc HOT 1
- np.int no longer works with numpy >= 1.24 HOT 2
- InvalidParameterError HOT 3
- mse needs updating (to friedman_mse?) HOT 1
- Question: Are plot_convergence and plot_objective supposed to look like identical plots?
- gp_minimize callback arguments / return value
- Bug Latin Hypercube sampling
- space.Integer and space.Float not allowed to be constant
- `np.int` was a deprecated alias for the builtin `int`. HOT 17
- First and last values of each variable in the space are sampled 2 times less than the others
- Repeated error Using Forest_optimize HOT 1
- Something about earlystopping HOT 1
- Optimization space and initial points in x0 use inconsistent dimensions.
- How to introduce customized stop criteria HOT 1
- Inclusion of PRIMA solvers in Skit-Optimize HOT 5
- LOO in BayesSearchCV
- Error in strategy-comparison.ipynb
- AttributeError: 'Sum' object has no attribute 'gradient_x' when using sci-kit learn RBF for sci-kit optimize gp_minimize HOT 3
- Question: How to initialize a model in 4D search space, but run trials in only 2D for the first 100 trials through ask-tell interface. Then expand to 4D.
- Reproducibility when using BayesSearchCV with MLPRegressor
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