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" )
usage=globals()["__doc__"],
)
parser.add_option("--config",
dest="filename_config",
type="string",
help="configuration file [default=%default].")
parser.set_defaults(filename_config="adda.ini", )
(options, args) = E.Start(parser)
if len(args) == 0:
raise ValueError("please supply one or more nids to test.")
config = AddaIO.ConfigParser()
config.read(os.path.expanduser(options.filename_config))
filename_graph = config.get("files", "output_graph", "adda.graph")
filename_index = config.get("files", "output_index", "adda.graph.index")
filename_fasta = config.get("files", "output_fasta", "adda")
fasta = IndexedFasta.IndexedFasta(filename_fasta)
index = cadda.IndexedNeighbours(filename_graph, filename_index)
config.set("files", "output_segments", "test.segments")
module = AddaSegment(
config=config,
fasta=fasta,
