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
cnlo_storage.normalize() cnlo_no3_storage.normalize() #all_storage.normalize() ts_storage = [kno3_1, kno3_2, kno3_3, kno3_4] #for s in ts_storage: #s.normalize() # Setup job manager jobman = JobManager(settings) # Figure out list of dex targets and put them into a network so we can # compare net = Network() net.gene_list = dex_storage.gene_list target = sys.argv[1] if target == "At1g25550": dex_storage = dex_storage2 for gene1 in dex_storage.gene_list: net.network[gene1] = {} for gene2 in dex_storage.gene_list: net.network[gene1][gene2] = 0 for gene in dex_storage.gene_list: print dex_storage.experiments[0].ratios[gene] if dex_storage.experiments[0].ratios[gene] >= 2.0: print dex_storage.experiments[0].ratios[gene] net.network[target][gene] = 1 elif dex_storage.experiments[0].ratios[gene] <= 0.5: