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R wrappers and tools for opencog meta-optimizing semantic evolutionary search
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DESCRIPTION
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README.md
Rmoses.Rproj
partition experiments.RData
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README.md

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Rmoses

R wrappers and tools for opencog's

Meta-Optimizing Semantic Evolutionary Search

Part of opencog, Meta-optimizing semantic evolutionary search" (MOSES) is a supervised machine learning system that generates programs in a simple lisp- like language to minimize a scoring function over the data to be catagorized. It's two level algorithm generates a population of sample programs or "demes" which are taken individually in the second level evolutionary loop and randomly "mutated" to generate better scoring programs untill improvement plateaus. The most successful programs are returned to the population and a new deme variant is selected for evolution. Programs are regularly normalized to reduce evaluation time and help keep code human-readable. The Rmoses package provides a wrapper for the seperately installed open source moses binary, interface functions with BioConductor bioinformatics packages, and a combo program translator for application to new data sets in the R environment.

run the moses binary

moses(flags = "", DSVfile = "", output = TRUE, ...)

  • flags: string of flags to pass to the binary (see moses binary man page)
    example: "-j8 --balance 1 -m 100000 -W 1 --output-cscore 1 --result-count 100"
  • DSVfile: moses input file (see man page for possible formats)
  • output, ...: output value is passed to system2(stdout = ), which runs the binary.
    the default value (TRUE) returns a character vector of the moses output. see the
    system2() help page for other values and other system2() variable options.

typical usage example

go to empty folder to hold moses input files and logs
setwd(~/path/to/moses_run)

make training partition csv files and list of corresponding testing partitions
listsOfDataPartitions <- makeMpartitions(mosesInput)

run moses on training sets
mosesOutput <- runMfolder("-j8 --balance 1 -m 100000 -W 1 -u case --output-cscore 1 --result-count 100")

extract combo strings and scores from moses output
combosNscores <- moses2combo(mosesOutput)

run combos on training and testing sets and generate confusion matrix
scoresNconfusionMatrix <- testClist(combosNscores$combo, listOfDataPartitions)

get combos scoring better than chance on test set testSetPerformance <- bestTestCombos(scoresNconfusionMatrix)

make dataframe of genes/feataures featureCount <- combo2fcount(testSetPerformance[[1]])

if test set scores are sufficiently high, get scores for complete data set combine fold results matrices and generate aggregate score and confusion matrix
aggScores <- mergeAggList(aggResults(scoresNconfusionMatrix))

the "aggScores" dataframe is ranked by score so combos can be filtered by row index

make dataframe of genes/feataures
aggFeatureCount <- combo2fcount(names(aggScores[[2]]))

TODO:
establish and implement a score cutoff to filter the combos
wrap workflow into single function
???

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