def run_all_subclone(args): time_start = time.time() ll_lst = [] subclone_num_lst = [] for subclone_num in range(1, 6): # run the joint model output_filename_base_k = args.output_filename_base + '_subclone_num_' + \ str(subclone_num) joint_model = JointProbabilisticModel(args.max_copynumber, subclone_num, args.baseline_thred) joint_model.read_data(args.input_filename_base) joint_model.preprocess() joint_model.run(args.max_iters, args.stop_value) joint_model.write_results(output_filename_base_k) ll_lst.append(joint_model.trainer.ll) subclone_num_lst.append(subclone_num) time_end = time.time() run_time = time_end - time_start get_summary(ll_lst, subclone_num_lst, run_time, args.output_filename_base)
def run_one_subclone(args): time_start = time.time() # run the joint model joint_model = JointProbabilisticModel(args.max_copynumber, args.subclone_num, \ args.baseline_thred) joint_model.read_data(args.input_filename_base) joint_model.preprocess() joint_model.run(args.max_iters, args.stop_value) joint_model.write_results(args.output_filename_base) time_end = time.time() print "*" * 100 print "* Finish." print "* Run time : {0:.2f} seconds".format(time_end - time_start) print "* Optimum log-likelihood : ", joint_model.trainer.ll print "*" * 100 sys.stdout.flush()