def read_output(self,settings):
# Code to write for collecting the output files from the algorithm, writes to the
# output list in the object
# What we want to do here is get the prediction rate on the last time
# point and the network so we can compare it against a gold std.
# This file is a bunch of zscores, so we have to load the cutoff we want
output_file = open(self.output_dir + "/output/ranked_edges.txt", 'r')
topn = None
if "top_n_edges" in settings["genie3"].keys():
topn = settings["genie3"]["top_n_edges"]
else:
topn = len(self.gene_list)
zscores = []
for line in output_file:
gene1, gene2, zscore = line.split()
zscore = float(zscore)
zscores.append((gene1, gene2, zscore))
zscores = sorted(zscores, key=lambda zscore: abs(zscore[2]), reverse=True)
self.zscores = zscores[:]
network = []
#for i,zscore in enumerate(zscores):
#if i < topn:
#gene1, gene2, zscore = zscore
#zscores[i] = (gene1, gene2, 1)
#else:
#gene1, gene2, zscore = zscore
#zscores[i] = (gene1, gene2, 0)
net = Network()
net.read_networklist(zscores)
net.gene_list = self.gene_list
self.network = net
return self.zscores
def read_output(self,settings):
# Code to write for collecting the output files from the algorithm, writes to the
# output list in the object
# What we want to do here is get the prediction rate on the last time
# point and the network so we can compare it against a gold std.
# This file is a bunch of zscores, so we have to load the cutoff we want
output_file = open(self.output_dir + "/output/mcz_output.txt", 'r')
output_file = output_file.readlines()
zscores = []
for g1, line in enumerate(output_file[1:]):
for g2, val in enumerate(line.split('\t')[1:]):
zscores.append((self.gene_list[g2], self.gene_list[g1], float(line.split()[1:][g2])))
topn = settings["mcz"]["top_n_edges"]
#for line in output_file:
#gene1, gene2, zscore = line.split()
#zscore = float(zscore)
#zscores.append((gene1, gene2, zscore))
zscores = sorted(zscores, key=lambda zscore: abs(zscore[2]), reverse=True)
self.zscores = zscores[:]
network = []
#for i,zscore in enumerate(zscores):
#if i < topn:
#gene1, gene2, zscore = zscore
#if zscore > 0:
#zscores[i] = (gene1, gene2, 1)
#if zscore < 0:
#zscores[i] = (gene1, gene2, -1)
#else:
#gene1, gene2, zscore = zscore
#zscores[i] = (gene1, gene2, 0)
net = Network()
net.read_networklist(zscores)
net.gene_list = self.gene_list
self.network = net
return self.network