def __init__(self, *args, **kwargs ):
AddaModuleBlock.__init__( self, *args, **kwargs )
self.mFilenameGraph = self.mConfig.get( "output", "graph", "adda.graph" )
self.mFilenameDomains = self.mConfig.get( "output", "domains", "adda.domains" )
self.mEvalueThresholdTrustedLinks = float(self.mConfig.get( "align", "evalue_threshold_trusted_links", -12.0 ))
self.mFilenameDomainGraph = self.mConfig.get( "output", "domaingraph", "adda.domaingraph.gz" )
cadda.setFilenameGraph( self.mFilenameGraph )
cadda.setFilenameDomains( self.mFilenameDomains )
cadda.setLogLevel( self.mLogLevel )
cadda.setEvalueThresholdTrustedLinks( self.mEvalueThresholdTrustedLinks )
self.mFilenames = (self.mFilenameDomainGraph, )
def __init__(self, *args, **kwargs):
AddaModuleBlock.__init__(self, *args, **kwargs)
self.mFilenameGraph = self.mConfig.get("files", "output_graph",
"adda.graph")
self.mFilenameDomains = self.mConfig.get("files", "output_domains",
"adda.domains")
self.mEvalueThresholdTrustedLinks = float(
self.mConfig.get("align", "evalue_threshold_trusted_links", -12.0))
self.mFilenameDomainGraph = self.mConfig.get("files",
"output_domaingraph",
"adda.domaingraph.gz")
cadda.setFilenameGraph(self.mFilenameGraph)
cadda.setFilenameDomains(self.mFilenameDomains)
cadda.setLogLevel(self.mLogLevel)
cadda.setEvalueThresholdTrustedLinks(self.mEvalueThresholdTrustedLinks)
self.mFilenames = (self.mFilenameDomainGraph, )
def applyMethod(self ):
"""index the graph.
"""
if self.isComplete(): return
self.info( "setting parameters" )
config = AddaIO.ConfigParser()
config.read( self.mFilenameFit )
self.mExponentialF = float( config.get( "optimise", "exponential_f" ) )
self.mExponentialE = float( config.get( "optimise", "exponential_e" ) )
cadda.setFilenameGraph( self.mFilenameGraph )
cadda.setFilenameIndex( self.mFilenameIndex )
cadda.setFilenameTransfers( self.mFilenameTransfers )
cadda.setFilenameDomains( self.mFilenameDomains )
cadda.setLogLevel( self.mLogLevel )
cadda.setReportStep( 1000 )
cadda.setMaxIterations( self.mMaxIterations )
cadda.setResolution( self.mResolution )
cadda.setExponentialF( self.mExponentialF )
cadda.setExponentialE( self.mExponentialE )
self.info( "optimisation started" )
cadda.dump_parameters()
retval = cadda.optimise_initialise()
if retval == 0:
self.warn( "initialisation failed" )
else:
self.info( "initialisation success" )
improvements = []
domains = [ self.mNSequences ]
for iteration in range( self.mMaxIterations ):
self.info( "iteration %i: started" % iteration)
t = time.time()
improvement = cadda.optimise_iteration()
if improvements:
rel_improvement = improvement / max(improvements)
else:
rel_improvement = 1
ndomains = cadda.optimise_get_num_partitions()
self.info( "iteration %i: finished in %i seconds: improvement=%f, relative improvement=%f, ndomains=%i" %\
(iteration,
time.time() - t,
improvement,
rel_improvement,
ndomains) )
if cadda.optimise_save_partitions( self.mFilenameDomains ):
self.info( "domains saved to %s" % self.mFilenameDomains)
else:
self.warn( "saving domains to %s failed" % self.mFilenameDomains)
improvements.append( improvement )
domains.append( ndomains )
self.plotProgress( improvements,
self.mOutputFilenameProgressImprovement,
"progress: improvement" )
self.plotProgress( domains,
self.mOutputFilenameProgressDomains,
"progress: domains" )
self.plotProgress( map( lambda x: float( x ) / self.mNSequences, domains),
self.mOutputFilenameProgressDomainsPerSequence,
"progress: domains per sequence" )
if improvement < self.mMinAbsImprovement:
self.info( "optimisation stopped because absolute improvement less than %f" %\
(self.mMinAbsImprovement) )
break
if rel_improvement < self.mMinRelImprovement:
self.info( "optimisation stopped because relative improvement less than %f" %\
(self.mMinRelImprovement) )
break
else:
self.info( "optimisation stopped because maximum iteration %i reached" %\
(self.mMaxIterations) )
retval = cadda.optimise_destroy()
if retval == 0:
self.warn( "destruction failed" )
else:
self.info( "destruction success" )