def launchsvdplus(self,input,submit_conf,output=None,npartitions=None,niterations=None,rank=None,
minval=None,maxval=None,gamma1=None,gamma2=None,gamma6=None,gamma7=None,storage_level=None,
scheduler_options=None,master=None):
default_settings = get_default_settings('svd')
if niterations is None:
niterations = default_settings.get('niterations')
if rank is None:
rank = default_settings.get('rank')
if minval is None:
minval = default_settings.get('minval')
if maxval is None:
maxval = default_settings.get('maxval')
if gamma1 is None:
gamma1 = default_settings.get('gamma1')
if gamma2 is None:
gamma2 = default_settings.get('gamma2')
if gamma6 is None:
gamma6 = default_settings.get('gamma6')
if gamma7 is None:
gamma7 = default_settings.get('gamma7')
if storage_level is None:
storage_level = default_settings.get('storage_level')
if output is None:
output = default_settings.get('output')
if npartitions is None:
npartitions = default_settings.get('npartitions')
class_params = [input,output,npartitions,niterations,rank,minval,maxval,gamma1,gamma2,gamma6,gamma7,storage_level]
jar_directory = self.home_directory + "SVDPlusPlus/target/SVDPlusPlusApp-1.0.jar"
self.submitter.submit(class_in_jar="src.main.scala.SVDPlusPlusApp",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchpca(self,input,submit_conf,dimensions=None,scheduler_options=None,master=None):
default_settings = get_default_settings('pca')
if dimensions is None:
dimensions = default_settings.get('dimensions')
class_params = [input,dimensions]
jar_directory = self.home_directory + "PCA/target/PCAApp-1.0.jar"
self.submitter.submit(class_in_jar="PCA.src.main.scala.PCAApp",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchgeneratetextfile(self,output,size,npartitions,submit_conf,
scheduler_options=None,master=None):
npoints = get_npoints_for_size('words',size)
default_settings = get_default_settings('generatetext')
class_params = [output,npoints,str(npartitions)]
jar_directory = self.home_directory + "BenchMark-1.0-SNAPSHOT.jar"
self.submitter.submit(class_in_jar="com.abrandon.upm.GenerateRandomText",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchngrams(self,input,submit_conf,output=None,scheduler_options=None,master=None):
default_settings = get_default_settings('ngrams')
if output is None:
output = default_settings.get('output')
class_params = [input,output]
jar_directory = self.home_directory + "BenchMark-1.0-SNAPSHOT.jar"
self.submitter.submit(class_in_jar="com.abrandon.upm.NGramsExample",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchgeneratedectreefile(self,output,size,npartitions,submit_conf,nfeatures=None,
scheduler_options=None,master=None):
npoints = get_npoints_for_size('decisiontree',size)
default_settings = get_default_settings('generatedectree')
if nfeatures is None:
nfeatures = default_settings.get('nfeatures')
class_params = [output,npoints,nfeatures,str(npartitions)]
jar_directory = self.home_directory + "BenchMark-1.0-SNAPSHOT.jar"
self.submitter.submit(class_in_jar="com.abrandon.upm.GenerateSVMData",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchgeneratepcafile(self,output,size,npartitions,submit_conf,nfeatures=None,
scheduler_options=None,master=None):
npoints = get_npoints_for_size('pca',size)
default_settings = get_default_settings('generatepca')
if nfeatures is None:
nfeatures = default_settings.get('nfeatures')
class_params = [output,npoints,nfeatures,str(npartitions)]
jar_directory = self.home_directory + "PCA/target/PCAApp-1.0.jar"
self.submitter.submit(class_in_jar="PCA.src.main.scala.PCADataGen",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchwordcount(self,input,submit_conf,output=None,npartitions=None,scheduler_options=None,master=None):
default_settings = get_default_settings('wordcount')
if output is None:
output = default_settings.get('output')
if npartitions is None:
npartitions = default_settings.get('npartitions')
class_params = [input,output,str(npartitions)]
jar_directory = self.home_directory + "BenchMark-1.0-SNAPSHOT.jar"
self.submitter.submit(class_in_jar="com.abrandon.upm.WordCount",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchstronglyconnectedcomponent(self,input,submit_conf,output=None,npartitions=None,scheduler_options=None,
master=None):
default_settings = get_default_settings('stronglyconnected')
if output is None:
output = default_settings.get('output')
if npartitions is None:
npartitions = default_settings.get('npartitions')
class_params = [input,output,npartitions]
jar_directory = self.home_directory + "StronglyConnectedComponent/target/StronglyConnectedComponentApp-1.0.jar"
self.submitter.submit(class_in_jar="src.main.scala.StronglyConnectedComponentApp",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchlinearregression(self,input,submit_conf,output=None,max_iterations=None,
scheduler_options=None,master=None):
default_settings = get_default_settings('linearregression')
if max_iterations is None:
max_iterations = default_settings.get('max_iterations')
if output is None:
output = default_settings.get('output')
class_params = [input,output,max_iterations]
jar_directory = self.home_directory + "LinearRegression/target/LinearRegressionApp-1.0.jar"
self.submitter.submit(class_in_jar="LinearRegression.src.main.java.LinearRegressionApp",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchgenerategraphfile(self,output,size,npartitions,submit_conf,scheduler_options=None,master=None,mu=None,sigma=None):
npoints = get_npoints_for_size('graph',size)
default_settings = get_default_settings('generategraph')
if mu is None:
mu = default_settings.get('mu')
if sigma is None:
sigma = default_settings.get('sigma')
class_params = [output,npoints,str(npartitions),mu,sigma]
jar_directory = self.home_directory + "common/target/Common-1.0.jar"
self.submitter.submit(class_in_jar="DataGen.src.main.scala.GraphDataGen",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchsvm(self,input,submit_conf,npartitions=None,niterations=None,
scheduler_options=None,master=None):
default_settings = get_default_settings('svm')
if niterations is None:
niterations = default_settings.get('niterations')
if npartitions is None:
npartitions = default_settings.get('npartitions')
class_params = [input,niterations,str(npartitions)]
jar_directory = self.home_directory + "BenchMark-1.0-SNAPSHOT.jar"
self.submitter.submit(class_in_jar="com.abrandon.upm.SupportVectorMachine",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchgroupby(self,submit_conf, num_kvpairs=None, value_size=None, nmappers=None,scheduler_options=None,master=None):
default_settings = get_default_settings('groupby')
if num_kvpairs is None:
num_kvpairs = default_settings.get('num_kvpairs')
if value_size is None:
value_size = default_settings.get('value_size')
if nmappers is None:
nmappers = default_settings.get('nmappers')
class_params = [num_kvpairs,value_size,nmappers]
jar_directory = self.home_directory + "BenchMark-1.0-SNAPSHOT.jar"
self.submitter.submit(class_in_jar="com.abrandon.upm.GroupByTest",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchtrianglecount(self,input,submit_conf,output=None,npartitions=None,storage_level=None,
scheduler_options=None,master=None):
default_settings = get_default_settings('trianglecount')
if storage_level is None:
storage_level = default_settings.get('storage_level')
if output is None:
output = default_settings.get('output')
if npartitions is None:
npartitions = default_settings.get('npartitions')
class_params = [input,output,npartitions,storage_level]
jar_directory = self.home_directory + "TriangleCount/target/TriangleCountApp-1.0.jar"
self.submitter.submit(class_in_jar="src.main.scala.triangleCountApp",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchshortestpaths(self,input,submit_conf,output=None,npartitions=None,numv=None,
scheduler_options=None,master=None):
default_settings = get_default_settings('shortestpaths')
if numv is None:
numv = default_settings.get('numv')
if output is None:
output = default_settings.get('output')
if npartitions is None:
npartitions = default_settings.get('npartitions')
class_params = [input,output,npartitions,numv]
jar_directory = self.home_directory + "ShortestPaths/target/ShortestPathsApp-1.0.jar"
self.submitter.submit(class_in_jar="src.main.scala.ShortestPathsApp",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchgeneratelinearregfile(self,output,size,npartitions,submit_conf,nfeatures=None,
eps=None, probone=None,scheduler_options=None,master=None):
npoints = get_npoints_for_size('linear',size)
default_settings = get_default_settings('generatelinearreg')
if nfeatures is None:
nfeatures = default_settings.get('nfeatures')
if eps is None:
eps = default_settings.get('eps')
if probone is None:
probone = default_settings.get('probone')
class_params = [output,npoints,nfeatures,eps,probone,str(npartitions)]
jar_directory = self.home_directory + "LinearRegression/target/LinearRegressionApp-1.0.jar"
self.submitter.submit(class_in_jar="LinearRegression.src.main.java.LinearRegressionDataGen",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchgeneratekmeansfile(self,output,size,npartitions,submit_conf,nclusters=None,
ndimensions=None, scaling=None,scheduler_options=None,master=None):
npoints = get_npoints_for_size('kmeans',size)
default_settings = get_default_settings('generatekmeans')
if nclusters is None:
nclusters = default_settings.get('nclusters')
if ndimensions is None:
ndimensions = default_settings.get('ndimensions')
if scaling is None:
scaling = default_settings.get('scaling')
class_params = [output,npoints,nclusters,ndimensions,scaling,str(npartitions)]
jar_directory = self.home_directory + "KMeans/target/KMeansApp-1.0.jar"
self.submitter.submit(class_in_jar="kmeans_min.src.main.scala.KmeansDataGen",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchkmeans(self,input,submit_conf,output=None,npartitions=None,nclusters=None,max_iterations=None,num_run=None,
scheduler_options=None,master=None):
default_settings = get_default_settings('kmeans')
if nclusters is None:
nclusters = default_settings.get('nclusters')
if max_iterations is None:
max_iterations = default_settings.get('max_iterations')
if num_run is None:
num_run = default_settings.get('num_run')
if output is None:
output = default_settings.get('output')
if npartitions is None:
npartitions = default_settings.get('npartitions')
class_params = [input,output,nclusters,max_iterations,num_run,str(npartitions)]
jar_directory = self.home_directory + "KMeans/target/KMeansApp-1.0.jar"
self.submitter.submit(class_in_jar="KmeansApp",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchpagerank(self,input,submit_conf,output=None,npartitions=None,max_iterations=None,tolerance=None,
reset_prob=None,storage_level=None,scheduler_options=None,master=None):
default_settings = get_default_settings('pagerank')
if max_iterations is None:
max_iterations = default_settings.get('max_iterations')
if tolerance is None:
tolerance = default_settings.get('tolerance')
if reset_prob is None:
reset_prob = default_settings.get('reset_prob')
if storage_level is None:
storage_level = default_settings.get('storage_level')
if output is None:
output = default_settings.get('output')
if npartitions is None:
npartitions = default_settings.get('npartitions')
class_params = [input,output,npartitions,max_iterations,tolerance,reset_prob,storage_level]
jar_directory = self.home_directory + "PageRank/target/PageRankApp-1.0.jar"
self.submitter.submit(class_in_jar="src.main.scala.pagerankApp",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
def launchdecisiontrees(self,input,submit_conf,output=None,nclasses=None,impurity=None,
max_depth=None,max_bins=None,mode=None,scheduler_options=None,master=None):
default_settings = get_default_settings('decisiontree')
if nclasses is None:
nclasses = default_settings.get('nclasses')
if impurity is None:
impurity = default_settings.get('impurity')
if max_depth is None:
max_depth = default_settings.get('max_depth')
if max_bins is None:
max_bins = default_settings.get('max_bins')
if mode is None:
mode = default_settings.get('mode')
if output is None:
output = default_settings.get('output')
class_params = [input,output,nclasses,impurity,max_depth,max_bins,mode]
jar_directory = self.home_directory + "DecisionTree/target/DecisionTreeApp-1.0.jar"
self.submitter.submit(class_in_jar="DecisionTree.src.main.java.DecisionTreeApp",class_params=class_params,
jar=jar_directory, master=master,submit_conf=submit_conf,scheduler_options=scheduler_options)
