def main(argList):
# Parse the arguments, which either come from the command line or a list
# provided by the Python code calling this function
parser = CreateParser()
(opts, args) = parser.parse_args(argList)
print "Parameters: %s" % opts
if opts.networkFile == "None":
raise RuntimeError("Must specify an network filename")
if opts.pathwaySource == "load" and (opts.pathwayPath == "None"
or opts.pathwayListFile == "None"):
raise RuntimeError(
"Must specify pathwayPath and pathwayListFile when loading pathways"
)
# Create the output path if needed
if not os.path.exists(opts.outPath):
print "Creating output directory %s" % opts.outPath
os.makedirs(opts.outPath)
# Load the interaction network
network = NetworkUtil.LoadNetwork(opts.networkFile, weight=True)
# Load or generate the pathways
if opts.pathwaySource == "load":
pathways = LoadPathways(opts.pathwayPath, opts.pathwayListFile)
elif opts.pathwaySource == "generate":
pathways = GeneratePathways(network, opts.numPathways, opts.branching,
opts.depth, opts.outPath, opts.name)
else:
# Shouldn't be able to get to this case
raise RuntimeError("%s is not a recognized pathway source" %
opts.pathwaySource)
# Sample from the pathways
CreateSamples(pathways, opts.samples, opts.fraction,
opts.outPath, opts.name, opts.noise, opts.sampleGroups,
set(network.nodes()))
def Evaluate(network2Pathway, outFileName, fraction, noise, weightedNetworks=False):
with open(outFileName, "w") as outFile:
npSum = 0
nrSum = 0
epSum = 0
erSum = 0
outFile.write("Steiner forest\tPathway\tTrue prizes\tNoisy prizes\tForest nodes\tPathway nodes\tIntersection nodes\tNode precision\tNode recall\tForest edges\tPathway edges\tIntersection edges\tEdge precision\tEdge recall\n")
# The name forestFile assumes the networks to evaluate are Steiner forests, but they can
# be any network
for forestFile, pathwayFile in network2Pathway:
# For each Steiner forest, compute the precision and recall with respect to the original pathway
forest = NetworkUtil.LoadNetwork(forestFile, weight=weightedNetworks)
# Remove the artificial node if the forest is not empty
if "DUMMY" in forest:
forest.remove_node("DUMMY")
# NetworkUtil.LoadNetwork only works for the simple format used when writing synthetic
# pathways. LoadGraphiteNetwork works for the simple format and the graphite edge list.
pathway = NetworkUtil.LoadGraphiteNetwork(pathwayFile)
intersection = NetworkUtil.Intersection(forest, pathway)
if forest.order() == 0:
nPrecision = 0
else:
nPrecision = float(intersection.order())/forest.order()
npSum += nPrecision
nRecall = float(intersection.order())/pathway.order()
nrSum += nRecall
if forest.size() == 0:
ePrecision = 0
else:
ePrecision = float(intersection.size())/forest.size()
epSum += ePrecision
eRecall = float(intersection.size())/pathway.size()
erSum += eRecall
truePrizes = int(math.ceil(fraction*pathway.order()))
noisyPrizes = int(math.ceil(noise*truePrizes))
outFile.write("%s\t%s\t%d\t%d\t%d\t%d\t%d\t%f\t%f\t%d\t%d\t%d\t%f\t%f\n" % (os.path.basename(forestFile), os.path.basename(pathwayFile), truePrizes, noisyPrizes, forest.order(), pathway.order(), intersection.order(), nPrecision, nRecall, forest.size(), pathway.size(), intersection.size(), ePrecision, eRecall))
# Write the average node/edge precision/recall
outFile.write("Average\t\t\t\t\t\t\t%f\t%f\t\t\t\t%f\t%f\n" % (npSum/len(network2Pathway), nrSum/len(network2Pathway), epSum/len(network2Pathway), erSum/len(network2Pathway)))
