print jobman.queue
jobman.runQueue()
jobman.waitToClear()
accs = []
out = open("output_runs.txt", 'w')
csv_out = open("MastersThesis.csv", 'w')
header = 'k,n_models,lambda_w,eta_z,tau,tp,tn,fp,fn,sensitivity,specificity,accuracy,fanins_correct,fanins_incorrect,fanouts_correct,fanouts_incorrect,cascades_correct,cascades_incorrect,feedforward_loops_correct,feedforward_loops_incorrect\n'
csv_out.write(header)
for i, job in enumerate(jobman.finished):
print job.alg.gene_list
print job.alg.gather_output(settings)
jobnet = Network()
for k in xrange(len(job.alg.gene_list) * len(job.alg.gene_list) + 1):
jobnet.read_netmatrix(job.alg.network, job.alg.gene_list, "timeseries")
jobnet.cutoff_network(k)
#print "\n\n\n\n\n\n"+"dfg4grn-small-net-test_ETAZ=" + str(p[0]) + "_LW="+str(p[1])+"_TAU="+str(p[2])
MastersThesis = jobnet.calculateAccuracy(goldnet)
report = jobnet.analyzeMotifs(goldnet)
print report.ToString()
out.write("dfg4grn-small-net-test_ETAZ-" + str(p[0]) + "_LW-"+str(p[1])+"_TAU-"+ str(p[2]) + "\n")
out.write(str(jobnet.calculateAccuracy(goldnet)))
out.write(report.ToString())
out.write("\n\n\n")
cstr = "{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10},{11},{12},{13},{14},{15},{16},{17},{18},{19},{20},{21}\n".format(
k,
job.alg.n_models,
job.alg.lambda_w,
job.alg.eta_z,
job.alg.tau,
