Uniform initialization interval

[nomisto]

Hello,

First of all thanks for your work, very helpful framework!

I've got a quick question about hyperparameter search and the uniform initialization interval. In the configs used for the paper you set

    - name: lookup_embedder.initialize_args.uniform_.a
      type: range
      bounds: [-1.0, -0.00001]

and in the table regarding the hyperparameter search space in the paper you say:

Interval (Unif) [−1.0,1.0]

so I would assume that b = -1 * a, however in the codebase it says the opposite (a = -1 * b), ist this a bug or did I overlook something?

kge/kge/model/kge_model.py

Lines 76 to 80 in d063477

	# Automatically set arg a (lower bound) for uniform_ if not given
	if initialize == "uniform_" and "a" not in initialize_args:
	initialize_args["a"] = initialize_args["b"] * -1
	KgeBase._initialize(what, initialize, initialize_args)

[druffinelli]

This is definitely a bug (whether in our configs or the code is a matter of interpretation). The idea was to have symmetric intervals, i.e. set a = -b, where a and b are lower and upper bound, respectively. The current code only sets the lower bound when it isn't given, meaning our search configs, which are setting the lower bound, are using the default value of the upper bound b = 1.0. The desired behavior can be obtained by setting "b" instead of "a" in the configs.

Codewise, I suggest a general solution that keeps the idea of using symmetric intervals. So, we'd set any non-given bound to be -1 times the given bound. We should use clearer names too, so lower_bound and upper_bound instead of torch's "a" and "b". Finally, we could decide to check that the given lower bound is indeed lower than the upper one, but torch let's that fly, so I suggest we do the same.

@rgemulla thoughts?

[rgemulla]

I think low/high is better than a/b. It's perhaps cleanest to disable any automatic setting of non-given values (and use defaults 0/1).

[druffinelli]

I now think the cleanest thing is to just get rid of the automatic setting of non-given values and leave the rest as is. Sure, using lower_bound/upper_bound would be clearer than torch's a and b, but it's nicer to leave a and b so the use of all initialization methods is consistent with torch's documentation.

@rgemulla if you agree, I'll drop the automatic settings. Otherwise, I'll add a line to rewrite the lower_bound/upper_bound keys as a and b to then pass it to torch

[rgemulla]

Yes, agreed. If only one is set, issue a warning with the actual bounds being used. If bounds are inconsistent, raise an error.

[druffinelli]

Inconsistent how? Pytorch actually does not allow the lower bound to be higher than the upper bound (I had tested it wrong before), so we may just want Pytorch to handle that

…

On Fri, 3 Dec 2021 at 14:12, Rainer Gemulla ***@***.***> wrote: Yes, agreed. If only one is set, issue a warning with the actual bounds being used. If bounds are inconsistent, raise an error. — You are receiving this because you were assigned. Reply to this email directly, view it on GitHub <#244 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABEWXZBXXTTUJL5R26LDCMDUPC645ANCNFSM5IG6WNHQ> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

[rgemulla]

Fine, as long as it's signaled via an error.