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Learning Boosted MLN with in-memory Relational Database integration (Malec et al. ILP 2016). This is an extension where wrapper ensures same command line argument structure as MLN-Boost. Most arguments are same as the original MLN-Boost(Khot et al. ICDM 2011) platform. Few that are different have been stated below.

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Title Author Original author excerpt
Basic Parameters
Mayukh Das
Marcin Malec
Basic usage overview MLNBoostDB.

License: [![][license img]][license]

Warning to all potential users - Under beta-test: Do not use/deploy on real applications

Note that this is for learning a Boosted MLN with in-memory Relational Database integration (Malec et al. ILP 2016). This is an extension where wrapper ensures same command line argument structure as MLN-Boost. Most arguments are same as the original MLN-Boost(Khot et al. ICDM 2011) platform. Few that are different have been stated below.

Warning!! Further note that this implementation DOES NOT WORK with PRECOMPUTES or derived precidates in the BK file. PLEASE REMOVE all precomputes and/or derived predicates that are not directly present in the evidence. Simple mode declarations should work perfectly.

Primary Runnable Binary

  • MLN-Boost-DB.jar

Download the whole repository for easy resolution of dependencies

If using souce code make sure to include all libraries in the lib folder in your build path

Simple Usage:

  • java -jar MLN-Boost-DB.jar [Args]

Arguments [Args]:

  • -l : enable training (learning).

  • -i : enable testing (inference).

  • -train <Training directory> : Path to the training directory in predicate logic format.

  • -test <Testing directory> : Path to the testing directory in predicate logic format format.

  • -model <Model directory> : Path to the directory with the stored models [or where they will be stored].

  • -target <target predicates> : Comma separated list of predicates to be learned/inferred.

  • -trees <Number of trees> : Number of Boosting trees aka Num of clauses in MLN.

  • -aucJarPath <path to auc.jar> : If this is not set, AUC values are not computed.

  • -mln : Set this flag, if you want to learn MLNs instead of RDNs

  • -mlnClauseLen : If -mlnclause is set, set the length of the clauses learned during each gradient step.

Additional arguments for Databse:

  • -dt <Database Type [hsqldb | H2]> : Choice of in-memory database to be used. 2 options available hsqldb (preferred) OR H2.

Paper:

Marcin Malec, Tushar Khot, James Nagy, Erik Blasch, and Sriraam Natarajan. Inductive Logic Programming meets Relational Databases: An Application to Statistical Relational Learning. In ILP 2016

Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude Shavlik.Learning Markov Logic Networks via Functional Gradient Boosting. In ICDM 2011.


Sample Calls:

Try to follow along with what each of these are doing:

From the Smokes-Friends-Cancer Dataset:

  • java -jar BoostSRL.jar -l -train ./Datasets/Toy-Cancer/train -model ./Datasets/Toy-Cancer/model -dt hsqldb -target cancer -i -test ./Datasets/Toy-Cancer/test -aucJarPath ./ -trees 20

Warning: Presently "hsqldb" works perfectly. Do not use the other database.

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Learning Boosted MLN with in-memory Relational Database integration (Malec et al. ILP 2016). This is an extension where wrapper ensures same command line argument structure as MLN-Boost. Most arguments are same as the original MLN-Boost(Khot et al. ICDM 2011) platform. Few that are different have been stated below.

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