fix bug for evaluating noise automatically for redundants points with noisy output when the inputs are mixed categorical #509
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smt/surrogate_models/krg_based.py
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| @@ -435,6 +435,18 @@ def _new_train(self): | |||
| if not self.options["eval_noise"]: | |||
| self.optimal_noise = np.array(self.options["noise0"]) | |||
| elif self.options["use_het_noise"]: | |||
| X = self.training_points[None][0][0] | |||
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X and y seems to be already set at the beginning of the function and the standardization seems to be done just above. I understand there are side-effects on X above but It is quite difficult to understand the flow between mixed-variables stuff and noise management. Maybe you should add comments to explain what is going on with X and y. And how mixed-variables and noise management are related. Does it make sense to have noise on a discrete variable?
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The noise is either global (noise0) and can be estimated automatically (eval_noise) or it is point-wise (hétéroscedastic) and given. It do not depends on the nature of the variables.
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Ok, so my question is why do you set X and y again and standardize again? Seems like mixed-variable handling above has an impact here otherwise this is already done.
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Sure, I'll update on that to change the standardization position and rename variables that are different!
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