laiprorus
commented
3 years ago Some additional observations.
Learning multiple target with RDN looks quite good. I could even learn ALL predicates for randomly generated Friends&Smokers dataset, with train_facts.txt being empty.
cmd args: -trees 10 -l -train datasets\Cancer\train\ -target friends,cancer,smokes
and BK:
useStdLogicVariables: true.
setParam: treeDepth=4.
setParam: nodeSize=2.
setParam: numOfClauses=8.
mode: friends(+Person, -Person).
mode: friends(-Person, +Person).
mode: smokes(+Person).
mode: cancer(+Person).
bridger: friends/2.
i then added
setParam: recursion=true.
mode: recursive_smokes(`Person).
okIfUnknown: recursive_smokes/1.
mode: recursive_cancer(`Person).
okIfUnknown: recursive_cancer/1.
and it worked well! it could find clauses such as smokes(a) <- friends(a,b),smokes(b)
thereroe i think i did set up the learning parameters and dataset properly.
But sadly nothing worked as soon as i tried to learn MLN by using same parameters and data and just adding -mln flag.
there errors are similar as above such as
here is the dataset from the run when this error happened
Toy-Cancer-All.zip
and cmd arguments -mln -trees 10 -l -train datasets\Toy-Cancer-All\train\ -target friends,cancer,smokes
boost-starai
Hello BoostSRL Team! After reading your paper Learning Markov Logic Networks via Functional Gradient Boosting http://pages.cs.wisc.edu/~tushar/papers/icdm11.pdf i have been trying to learn MLN with multiple targets. But so far i did not have much success. How do i set up the learning flags and my data properly? Here is what i have tried to do.
Toy-Father
I have manually added female and mother predicates. For Tree-Based i run following flags:
It appears that it is generating wrong examples (mother example for father target)
-mln -trees 10 -l -train datasets\Father-Mother\train\ -target father,motherthe result is 2 models but they appear to be the same as if i learned each target seperatly. I was hoping to see a Joint Model where the 2 models would somehow influence each other. For Clause-Based i used:-mln -mlnClause -trees 10 -l -train datasets\Father-Mother\train\ -target father,motherBut the code crashes with following error:Toy-Cancer
I was trying to learn a Joint Model for Cancer dataset. In default setting it is meant to learn just the predicate cancer.
In both Tree-Based and Clause-Based settings.
Even trying to learn 2 predicates out of 3 had similar error messages.
train_factscontains only friends and smokes predicates andtrain_pos/train_negcontaints exampels for _cancer. How do i set up to learn all 3 predicates (friends, smokes, cancer) at once? I tried moving all the facts totrain_pos/train_negor duplicating them intrain_factsandtrain_pos/train_neg(with proper negative examples) but all i was getting were erros like this:Cora
In your paper you have results for both Tree-Based and Clause-Based approaches on Cora datasets when learning Joint Model for Cora dataset with target predicates
SameBib,SameVenue,SameTitleandSameAuthor. I tried to do Clause-Base learning:-mln -mlnClause -trees 10 -l -train datasets\Cora\train\ -target sameauthor,samebib,sametitle,samevenueBut after less than a minute i get an error:I looked at the source code and wiki/documentation and didnt find much on working with multiple targets. Since you got the results in your paper i do really hope that you can help me. Thank you in advance from D.Ravdin!