How can I use deepdive to perform unsupervised verification?

[rudaoshi]

Hi, I have a unlabeled data set in which each sample has an initial confidence. I want to do some verification by writing rules about commonsense to reduce the confidences of samples which violate commonsense. How can I do this?

1) How can I use the initial confidence? 2) How can I assign weights to the commonsense rules?

Thank you very much!

[rudaoshi]

When I want to define a unsupervised model by using "p(x,y)=NULL: ....", the program report:

column "label" is of type boolean but expression is of type text

It seems that the error is about a bug in sql generation module.

[chrismre]

You may want to check out http://hazyresearch.github.io/snorkel/, which is more directly about weak supervision.

DeepDive can do all this (and much more!). This flexibility means that not all representation decisions are obvious.

Chris

On Mon, Dec 19, 2016 at 8:24 PM 孙明明 notifications@github.com wrote:

When I want to define a unsupervised model by using "p(x,y)=NULL: ....", the program report:

column "label" is of type boolean but expression is of type text

It seems that the error is about a bug in sql generation module.

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/HazyResearch/deepdive/issues/608#issuecomment-268151386, or mute the thread https://github.com/notifications/unsubscribe-auth/AHPtuEFo-aPa3pwU-pGppjuv12ulh3Pfks5rJ1iIgaJpZM4LRaM2 .

[rudaoshi]

@chrismre Thank you for mentioning another amazing projects. I'll look at it.

However, currently I'd rather like to know how to do this by deepdive.

[chrismre]

DeepDive can express essentially any factor graph. You'll need to write the rules that create the required factor graph. A default factor graph for this process is described in the Snorkel/data programming paper.

Hope that helps! Chris

[alldefector]

@rudaoshi Here is an example rule with manually assigned weights (adapted from the "census" example):

@weight(1.2)
rich(id) :- adult(id, _, workclass, _, _, _, _, _, _, _, _, _, _, _, _, income_bracket).

You can change the rule body (the adult... part) to encode the "common sense" cases.

[thodrek]

@rudaoshi Regarding the NULL issue alone, you should try to use casting within ddlog. Writing = NULL::boolean instead of = NULL will take care of the label casting issue.

[rudaoshi]

Thank you very much to every one. I'll try your recommended solutions.

[rudaoshi]

@thodrek I got following error using Deepdive 0.8:

2016-12-21 11:43:00.679659 [error] app.ddlog[176.44] failure: :-' expected but:' found 2016-12-21 11:43:00.679741 2016-12-21 11:43:00.679756 has_relation(p1_id, p2_id, relation) = NULL:boolean 2016-12-21 11:43:00.679767 2016-12-21 11:43:00.679947 ^

Does this feature need newer version cloned from github?

[alldefector]

@rudaoshi note that there are two colons: NULL::boolean. If that doesn't work, try cast(null as boolean).