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'], MastersThesis['fp'],