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Basic Usage Parameters
By: Tushar Khot, Sriraam Natarajan
<< "File Structure" | BoostSRL Wiki | "Advanced Usage Parameters" >>
Note that this is for learning an RDN. The basic version boosts a single conditional relational probability distribution.
java -cp BoostSRL.jar edu.wisc.cs.Boosting.RDN.RunBoostedRDN [Args]java -jar BoostSRL.jar [Args]
- 
-l: enable training (learning). - 
-i: enable testing (inference). - 
-noBoost: disable Boosting (i.e., learns a single relational regression tree). - 
-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].Default location: "Training directory"/models
 - 
-target <target predicates>: Comma separated list of predicates to be learned/inferred. - 
-trees <Number of trees>: Number of Boosting trees.Default: 20. Ignored if
-noBoostis set. - 
-step <Step Length>: Default step length for functional gradient.Default: 1.
 - 
-modelSuffix <suffix>: All the trees/models are saved with this suffix appended to the file names. - 
-aucJarPath <path to auc.jar>: If this is not set, AUC values are not computed. - 
-testNegPosRatio <Negative/Positive ratio>: Ratio of negatives to positive for testing.Default: 2. Set to -1 to disable sampling.
 
Try to follow along with what each of these are doing:
From the Getting Started tutorial:
- 
java -jar BoostSRL.jar -l -combine -train train/ -target father -trees 10 - 
java -jar BoostSRL.jar -i -model train/models -test test/ -target father -trees 10 
From the Boston Housing Dataset (notice the different classpath):
java -cp BoostSRL.jar edu.wisc.cs.will.Boosting.Regression.RunBoostedRegressionTrees -reg -l -train train/ -target medv -trees 20
From the CiteSeer Dataset:
java -jar BoostSRL.jar -l -train train/ -target infield_fauthor,infield_ftitle,infield_fvenue -trees 5
<< "File Structure" | BoostSRL Wiki | "Advanced Usage Parameters" >>
BoostSRL Wiki
Home
BoostSRL Basics
- Getting Started
 - File Structure
 - Basic Usage Parameters
 - Advanced Usage Parameters
 - Basic Modes Guide
 - Advanced Modes Guide
 
Deep dive into BoostSRL
- Default (RDN-Boost)
 - MLN-Boost
 - Regression
 - Cost-sensitive SRL
 - Learning with Advice
 - Approximate Counting
 - One-class Classification (coming soon)
 - Discretization of Continuous Valued Attributes
 - Lifted Relational Random Walks
 - Grounded Relational Random Walks
 
Datasets
Applications of BoostSRL