def main():
""" Runs independently from command line to test the GAssist algorithm. """
graphPerformance = False # Built in graphing ability, currently not functional, but mechanism is in place.
trainData = "2_1000_0_1600_0_0_CV_0_Train.txt"
testData = "2_1000_0_1600_0_0_CV_0_Test.txt"
outProg = "GH_GAssist_ProgressTrack"
outPop = "GH_GAssist_PopulationOut"
bitLength = 1 # This implementation is not yet set up to handle other rule representations, or bit encoding lengths.
CVpartitions = 10
trackCycles = 1
# Run Parameters - User specified.
iterInput = '20.50.100'
pop = 100
wild = 0.5
defaultClass = "0" #auto, 0, disabled
init = "cw" #'none', 'smart', 'cw'
MDL = 1
windows = 2
#Figure out the iteration stops for evaluation, and the max iterations.
iterList = iterInput.split('.')
for i in range(len(iterList)):
iterList[i] = int(iterList[i])
lastIter = iterList[len(iterList)-1]
#Sets up up algorithm to be run.
e = GAssist_Environment(trainData,testData,bitLength, init)
cons.setConstants(pop, wild, defaultClass, e, MDL, windows)
sampleSize = e.getNrSamples()
gassist = GAssist(e, outProg, outPop, bitLength, CVpartitions, graphPerformance)
#Set some GAssist parameters.
if trackCycles == 'Default':
gassist.setTrackingIterations(sampleSize)
else:
gassist.setTrackingIterations(trackCycles)
gassist.setNumberOfTrials(lastIter, iterList)
gassist.setInitialization(init)
#Run the GAssist Algorithm
gassist.runGAssist()
12: Wild frequency - 0.5, 0.75
13: Default Class Handeling - auto, 0, disabled - automatic default class evolution, vs. pre-specify the default class (can also add a method which chooses via which class is most represented.
14: MDL fitness - '0', '1' - where 0 is False, and 1 is True.
15: Windows - '1', '2', '4' - number of partitions of the dataset used in the learning process. (higher values tend to promote generalization, and reduce computational time.)
"""
# *************************************************************************************************
graphPerformance = False # Built in graphing ability, currently not functional, but mechanism is in place. NOT MEANT TO BE USED ON CLUSTER.
numArgs = len(argv)
print "Arguments: " + str(numArgs)
if numArgs == 16:
if argv[1] == 'gh': #Different rule representations could be programmed but have not been in this implementation.
print ("Format Training data: "+argv[2]+" using a "+argv[6]+" bit coding scheme.")
#Sets up up algorithm to be run.
e = GAssist_Environment(str(argv[2]), str(argv[3]), int(argv[6]), str(argv[10]))
cons.setConstants(int(argv[11]), float(argv[12]), str(argv[13]), e, str(argv[14]), str(argv[15]))
sampleSize = e.getNrSamples()
gassist = GAssist(e, argv[4], argv[5], int(argv[6]), int(argv[7]), graphPerformance)
#Figure out the iteration stops for evaluation, and the max iterations.
iterList = argv[9].split('.')
for i in range(len(iterList)):
iterList[i] = int(iterList[i])
lastIter = iterList[len(iterList)-1]
#Set some GAssist parameters.
if argv[9] == 'Default':
gassist.setTrackingIterations(sampleSize)
else:
gassist.setTrackingIterations(int(argv[8]))
