cojob = ConvexOptimization()
cojob.setup(knockout_storage, settings, "MCZ-DFG_Test_Top_{0}_Edges".format(i), job.alg.network)
jobman.queueJob(cojob)
jobman.runQueue()
jobman.waitToClear()
rocs = []
accs.append("Convex Opt + MCZ prior:")
for job in jobman.finished:
jobnet = job.alg.network
print "PREDICTED NETWORK:"
print jobnet.network
print "GOLDEN NETWORK:"
print goldnet.network
job.alg.save()
rocs.append(GenerateROC(jobnet, goldnet))
threshnet = Network(jobnet)
threshnet.network = threshnet.apply_threshold(0)
accs.append(threshnet.calculateAccuracy(goldnet))
#print jobnet.analyzeMotifs(goldnet).ToString()
PlotMultipleROC(rocs, 'ConvexOpt + MCZ')
for row in accs:
print row
print "AOCS"
for r in rocs:
print r.auc()
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,
MastersThesis['tp'],
MastersThesis['tn'],
out = open("output_runs.txt", 'w')
csv_out = open("results.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)
max_n_genes = 500
print "Gathering results into results.csv..."
for i, job in enumerate(jobman.finished):
#print job.alg.gene_list
#print job.alg.gather_output(settings)
jobnet = Network()
for k in xrange(max_n_genes):
print "On iteration ", k+1, " of ", max_n_genes
jobnet.read_netmatrix(job.alg.network, job.alg.gene_list, True)
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])
results = 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,
results['tp'],
results['tn'],
