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"),
int(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",
cols=["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"],
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)
if VERBOSE:
print("\n", ht.now(), 'Result dataframe has been constructed...')
result_4digit = result.applymap(get_types)
for iii in ["A1", "A2", "B1", "B2", "C1", "C2"]:
if not iii in result_4digit:
result_4digit[iii] = None
r = result_4digit[["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"]]
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",
cols=["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"],
header=["A1", "A2", "B1", "B2", "C1", "C2", "Reads", "Objective"])
hlatype = result.irow(0)[["A1", "A2", "B1", "B2", "C1", "C2"]].drop_duplicates()
features_used = [('intron', 1), ('exon', 2), ('intron', 2), ('exon', 3), ('intron', 3)] \
if not args.rna else [('exon',2),('exon',3)]