minimal_alleles = ht.prune_overshadowed_alleles(temp_pruned)
if VERBOSE:
print("\n", ht.now(),
'Keeping only the minimal number of required alleles',
minimal_alleles.shape)
binary = binary[minimal_alleles]
if VERBOSE:
print("\n", ht.now(), 'Creating compact model...')
if is_paired and unpaired_weight > 0:
if use_discordant:
compact_mtx, compact_occ = ht.get_compact_model(
binary_p[minimal_alleles],
pd.concat([binary_un, binary_mis])[minimal_alleles],
weight=unpaired_weight)
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)
temp_pruned = ht.prune_identical_reads(unique_col)
if args.verbose:
print "\n", ht.now(), 'Size of mtx with unique rows and columns:', temp_pruned.shape
print ht.now(), 'determining minimal set of non-overshadowed alleles'
minimal_alleles = ht.prune_overshadowed_alleles(temp_pruned)
if args.verbose:
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)
if args.verbose:
print "\n", ht.now(
), 'Size of mtx with unique rows and columns:', temp_pruned.shape
print ht.now(), 'determining minimal set of non-overshadowed alleles'
minimal_alleles = ht.prune_overshadowed_alleles(temp_pruned)
if args.verbose:
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,
print("\n", ht.now(), 'Size of mtx with unique rows and columns:', temp_pruned.shape)
print(ht.now(), 'determining minimal set of non-overshadowed alleles')
minimal_alleles = ht.prune_overshadowed_alleles(temp_pruned)
if VERBOSE:
print("\n", ht.now(), 'Keeping only the minimal number of required alleles', minimal_alleles.shape)
binary = binary[minimal_alleles]
if VERBOSE:
print("\n", ht.now(), 'Creating compact model...')
if is_paired and unpaired_weight > 0:
if use_discordant:
compact_mtx, compact_occ = ht.get_compact_model(binary_p[minimal_alleles],
pd.concat([binary_un, binary_mis])[minimal_alleles], weight=unpaired_weight)
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"))
