def LoadPathways(pathwayPath, listFile):
pathways = []
with open(listFile) as inFile:
for pathwayLine in inFile:
pathwayLine = pathwayLine.strip()
# Each line is a relative path to a pathway file
pathway = NetworkUtil.LoadGraphiteNetwork(
os.path.join(pathwayPath, pathwayLine))
pathway.graph["filename"] = os.path.join(pathwayPath, pathwayLine)
if pathwayLine.endswith(".txt"):
pathwayLine = pathwayLine[0:-4] # Remove ".txt"
pathway.graph["name"] = pathwayLine
# Debugging
print "Loaded %s with %d nodes and %d edges" % (
pathway.graph["name"], pathway.order(), pathway.size())
# Add the pathway to the list
pathways.append(pathway)
return pathways
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)))
