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",