fulltrainset = Instances(trainfile)
fulltrainset.setClassIndex(fulltrainset.numAttributes() - 1)
testset = Instances(testfile)
testset.setClassIndex(testset.numAttributes() - 1)
# open output files
bufsize=0
classifiername = str(os.path.splitext(os.path.basename(__file__))[0])
dataname = str(os.path.splitext(os.path.basename(sys.argv[1]))[0])
datafilelimit = "data/plot/" + classifiername + "_" + dataname + crossvalidate + "_instances.csv"
filelimit=open(datafilelimit, 'w', bufsize)
filelimit.write("instances,pctincorrecttest,pctincorrecttrain\n")
logfile = "logs/" + classifiername + "_" + dataname + crossvalidate + ".log"
log=open(logfile, 'w', bufsize) # open general log file
for num in range(int(p['j48.initial']),fulltrainset.numInstances(),(fulltrainset.numInstances() / int(p['j48.numdatapoints']))):
filelimit.write(str(num))
trainset = Instances(fulltrainset,0,num) # create training set
trainset.setClassIndex(trainset.numAttributes() - 1)
log.write("---------------------------------\nTraining Set Size: " + str(trainset.numInstances()) + ", Test Set Size: " + str(testset.numInstances()) + ", Full data set size: " + str(fulltrainset.numInstances()) + "\n")
for dataset in [testset, fulltrainset]:
algo = J48()
algo.buildClassifier(trainset)
algo.setConfidenceFactor(float(p['j48.C']))
evaluation = Evaluation(trainset)
output = PlainText() # plain text output for predictions
output.setHeader(trainset)
buffer = StringBuffer() # buffer to use
output.setBuffer(buffer)
attRange = Range() # no additional attributes output
outputDistribution = Boolean(False) # we don't want distribution
print "Usage: supervised.py <ARFF-file>"
sys.exit()
# load data file
print "Loading data..."
datafile = FileReader(sys.argv[1])
data = Instances(datafile)
rand = Random() # seed from the system time
data.randomize(rand) # randomize data with number generator
# open output files
bufsize=0
dataname = str(os.path.splitext(os.path.basename(sys.argv[1]))[0])
# loop for different amounts of data with fixed test set
datasize = data.numInstances()
limit = (datasize*2)/3 # loop until we use 2/3 data as training set
testset = Instances(data,limit,datasize-limit) # create training set using the last 1/3 of data
testset.setClassIndex(testset.numAttributes() - 1)
saver = ArffSaver()
saver.setInstances(testset)
testsetfile = "./data/split/" + dataname + "-" + "test.arff"
file = File(testsetfile)
saver.setFile(file)
saver.writeBatch()
trainset = Instances(data,0,limit) # create training set
saver = ArffSaver()
saver.setInstances(trainset)
trainsetfile = "./data/split/" + dataname + "-" + "train.arff"
testset = Instances(testfile)
testset.setClassIndex(testset.numAttributes() - 1)
# open output files
bufsize = 0
classifiername = str(os.path.splitext(os.path.basename(__file__))[0])
dataname = str(os.path.splitext(os.path.basename(sys.argv[1]))[0])
datafilelimit = "data/plot/" + classifiername + "_" + dataname + crossvalidate + "_instances.csv"
filelimit = open(datafilelimit, 'w', bufsize)
filelimit.write(
"instances,lineartest,lineartrain,polytest,polytrain,radialtest,radialtrain,sigmoidtest,sigmoidtrain\n"
)
logfile = "logs/" + classifiername + "_" + dataname + crossvalidate + ".log"
log = open(logfile, 'w', bufsize) # open general log file
for num in range(int(p['svm.initial']), fulltrainset.numInstances(),
(fulltrainset.numInstances() / int(p['svm.numdatapoints']))):
trainset = Instances(fulltrainset, 0, num) # create training set
trainset.setClassIndex(trainset.numAttributes() - 1)
filelimit.write(str(num))
for kerneltype in range(0, 4):
log.write("---------------------------------\nTraining Set Size: " +
str(trainset.numInstances()) + ", Test Set Size: " +
str(testset.numInstances()) + ", Full data set size: " +
str(fulltrainset.numInstances()) + "\n")
for dataset in [testset, fulltrainset]:
algo = LibSVM()
tag = SelectedTag(
str(kerneltype), algo.TAGS_KERNELTYPE
) # 0 = linear, 1 = polynomial, 2 = radial basis function, 3 = sigmoid
testset.setClassIndex(testset.numAttributes() - 1)
# open output files
bufsize = 0
classifiername = str(os.path.splitext(os.path.basename(__file__))[0])
dataname = str(os.path.splitext(os.path.basename(sys.argv[1]))[0])
datafilelimit = "data/plot/" + classifiername + "_" + dataname + crossvalidate + "_instances.csv"
filelimit = open(datafilelimit, 'w', bufsize)
filelimit.write("instances,pctincorrecttest,pctincorrecttrain\n")
logfile = "logs/" + classifiername + "_" + dataname + crossvalidate + ".log"
log = open(logfile, 'w', bufsize) # open general log file
timefilename = "data/plot/" + classifiername + "_" + dataname + crossvalidate + "_traintime.csv"
timefile = open(timefilename, 'w', bufsize)
timefile.write("instances,timetest,timetrain\n")
for num in range(int(p['mlp.initial']), fulltrainset.numInstances(),
(fulltrainset.numInstances() / int(p['mlp.numdatapoints']))):
trainset = Instances(fulltrainset, 0, num) # create training set
trainset.setClassIndex(trainset.numAttributes() - 1)
log.write("---------------------------------\nTraining Set Size: " +
str(trainset.numInstances()) + ", Test Set Size: " +
str(testset.numInstances()) + ", Full data set size: " +
str(fulltrainset.numInstances()) + "\n")
filelimit.write(str(trainset.numInstances()))
timefile.write(str(num))
for dataset in [testset, fulltrainset]:
algo = MultilayerPerceptron()
algo.setTrainingTime(int(p['mlp.N']))
x = time.time()
algo.buildClassifier(trainset)
testset.setClassIndex(testset.numAttributes() - 1)
# open output files
bufsize=0
classifiername = str(os.path.splitext(os.path.basename(__file__))[0])
dataname = str(os.path.splitext(os.path.basename(sys.argv[1]))[0])
datafilelimit = "data/plot/" + classifiername + "_" + dataname + crossvalidate + "_instances.csv"
filelimit=open(datafilelimit, 'w', bufsize)
filelimit.write("instances,pctincorrecttest,pctincorrecttrain\n")
logfile = "logs/" + classifiername + "_" + dataname + crossvalidate + ".log"
log=open(logfile, 'w', bufsize) # open general log file
timefilename = "data/plot/" + classifiername + "_" + dataname + crossvalidate + "_traintime.csv"
timefile = open(timefilename, 'w', bufsize)
timefile.write("instances,timetest,timetrain\n")
for num in range(int(p['adaboost.initial']),fulltrainset.numInstances(),(fulltrainset.numInstances() / int(p['adaboost.numdatapoints']))):
trainset = Instances(fulltrainset,0,num) # create training set
trainset.setClassIndex(trainset.numAttributes() - 1)
log.write("---------------------------------\nTraining Set Size: " + str(trainset.numInstances()) + ", Test Set Size: " + str(testset.numInstances()) + ", Full data set size: " + str(fulltrainset.numInstances()) + "\n")
filelimit.write(str(num))
timefile.write(str(num))
for dataset in [testset, fulltrainset]:
algo = AdaBoostM1()
weaklearner = J48()
algo.setClassifier(weaklearner)
algo.setNumIterations(int(p['adaboost.iterations']))
x = time.time()
algo.buildClassifier(trainset)
evaluation = Evaluation(trainset)
timefile.write("," + str(time.time() - x))
output = PlainText() # plain text output for predictions
print "Usage: supervised.py <ARFF-file>"
sys.exit()
# load data file
print "Loading data..."
datafile = FileReader(sys.argv[1])
data = Instances(datafile)
rand = Random() # seed from the system time
data.randomize(rand) # randomize data with number generator
# open output files
bufsize = 0
dataname = str(os.path.splitext(os.path.basename(sys.argv[1]))[0])
# loop for different amounts of data with fixed test set
datasize = data.numInstances()
limit = (datasize * 2) / 3 # loop until we use 2/3 data as training set
testset = Instances(data, limit, datasize -
limit) # create training set using the last 1/3 of data
testset.setClassIndex(testset.numAttributes() - 1)
saver = ArffSaver()
saver.setInstances(testset)
testsetfile = "./data/split/" + dataname + "-" + "test.arff"
file = File(testsetfile)
saver.setFile(file)
saver.writeBatch()
trainset = Instances(data, 0, limit) # create training set
saver = ArffSaver()
saver.setInstances(trainset)
