else: compact_mtx, compact_occ = ht.get_compact_model( binary_p[minimal_alleles], binary_un[minimal_alleles], weight=unpaired_weight) else: compact_mtx, compact_occ = ht.get_compact_model(binary) allele_ids = binary.columns groups_4digit = defaultdict(list) for allele in allele_ids: type_4digit = get_4digit(allele) groups_4digit[type_4digit].append(allele) sparse_dict = ht.mtx_to_sparse_dict(compact_mtx) threads = get_num_threads(config.getint("ilp", "threads")) if VERBOSE: print("\nstarting ilp solver with %s threads..." % threads) print("\n", ht.now(), 'Initializing OptiType model...') op = OptiType(sparse_dict, compact_occ, groups_4digit, table, args.beta, 2, config.get("ilp", "solver"), threads, verbosity=VERBOSE) result = op.solve(args.enumerate)
print "\n", ht.now(), 'Keeping only the minimal number of required alleles', minimal_alleles.shape binary = binary[minimal_alleles] if args.verbose: print "\n", ht.now(), 'Creating compact model...' compact_mtx, compact_occ = ht.get_compact_model(binary) allele_ids = binary.columns groups_4digit = defaultdict(list) for allele in allele_ids: type_4digit = get_4digit(allele) groups_4digit[type_4digit].append(allele) sparse_dict = ht.mtx_to_sparse_dict(compact_mtx) if args.verbose: print "\n", ht.now(), 'Initializing OptiType model...' op = OptiType(sparse_dict, compact_occ, groups_4digit, table, args.beta, 2, config.get("OPTIMIZATION", "SOLVER"), config.get("OPTIMIZATION", "THREADS"), verbosity=verbosity) result = op.solve(args.enumerate) if args.verbose: print "\n", ht.now(), 'Result dataframe has been constructed...' result_4digit = result.applymap(get_types) r = result_4digit[["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"]] #write CSV to out. and generate Plots. r.to_csv(out_csv, sep="\t",