Learning the structure of graphical models from datasets with thousands of variables More information about the research papers detailing the theory behind Chordalysis is available at http://www.francois-petitjean.com/Research
Underlying research and scientific papersThis code is supporting 5 research papers:
When using this repository, please cite:
@ARTICLE{Capdevila2018-Behaviormetrika,
author = {Capdevila, Joan and Zhao, He and Petitjean, Francois and Buntine, Wray},
title = {Experiments with learning graphical models on text},
journal = {Behaviormetrika},
year = {2018},
month = {May},
day = {08},
doi = {10.1007/s41237-018-0050-3},
url = {https://doi.org/10.1007/s41237-018-0050-3}
}
@INPROCEEDINGS{Petitjean2016-KDD,
author = {Webb, Geoffrey I. and Petitjean, Francois},
title = {A multiple test correction for streams and cascades of statistical hypothesis tests},
booktitle = {ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = 2016,
pages = {1225--1264}
}
@INPROCEEDINGS{Petitjean2015-SDM,
author = {Petitjean, Francois and Webb, Geoffrey I.},
title = {Scaling log-linear analysis to datasets with thousands of variables},
booktitle = {SIAM International Conference on Data Mining},
year = 2015,
pages = {469--477}
}
@INPROCEEDINGS{Petitjean2014-ICDM-1,
author = {Petitjean, Francois and Allison, Lloyd and Webb, Geoffrey I. and Nicholson, Ann E.},
title = {A statistically efficient and scalable method for log-linear analysis of high-dimensional data},
booktitle = {IEEE International Conference on Data Mining},
year = 2014,
pages = {480--489}
}
@INPROCEEDINGS{Petitjean2013-ICDM,
author = {Petitjean, Francois and Webb, Geoffrey I. and Nicholson, Ann E.},
title = {Scaling log-linear analysis to high-dimensional data},
booktitle = {IEEE International Conference on Data Mining},
year = 2013,
pages = {597--606}
}
Chordalysis requires Java 8 (to run) and Ant (to compile); other supporting library are providing in the lib folder (with associated licenses).
git clone https://github.com/fpetitjean/Chordalysis
cd Chordalyis
ant compile
Simply entering ant jar will create a jar file that you can execute in most environments in bin/jar/Chordalyis.jar. Normal execution would then look like java -jar -Xmx1g bin/jar/Chordalysis.jar Note that Xmx1g means that you are allowing the Java Virtual Machine to use 1GB - althought this is ok for most datasets, please increase if your dataset is large.
The compile command creates all .class files in the bin/ directory. To execute the demos, simply run:
java -Xmx1g -classpath bin:lib/core/commons-math3-3.2.jar:lib/core/jayes.jar:lib/core/jgrapht-jdk1.6.jar:lib/extra/jgraphx.jar:lib/loader/weka.jar demo.RunGUIProof
This will run the GUI, which will take you through choosing the different options.
If you want to run everythin in command line, please run:
java -Xmx1g -classpath bin:lib/core/commons-math3-3.2.jar:lib/core/jayes.jar:lib/core/jgrapht-jdk1.6.jar:lib/extra/jgraphx.jar:lib/loader/weka.jar demo.Run dataFile pvalue imageOutputFile useGUI
where:
dataFile represents the path to your dataset in CSV format (eg /home/doe/mydata.csv)pvalue represents the maximum family-wise error rate (FWER); usually 0.05imageOutputFile represents the path to the output graph file as an image (eg /home/doe/mygraph.png)useGUI is a boolean used to display the output graph in a GUI or not (eg false if you want everything in command line)There are other demos, allowing you to, for instance, export the probability tables, play with belief propagation, or load a dataset in .arff format. Please just contact me if you need help.
We now have an R interface for Chordalysis, see:
YourKit is supporting Chordalysis open source project with its full-featured Java Profiler. YourKit is the creator of innovative and intelligent tools for profiling Java and .NET applications. http://www.yourkit.com
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