def main(argv):
# global exp_setting
exp_setting = Configuration()
debug_mode = list()
reduced_mode = False
test_mode = False
seq_type = None
gene_prot = None
feature_set = list()
percentile_range = list()
args = parser.parse_args(argv[1:])
if len(argv) <= 1:
parser.parse_args(['--help'])
return
# set version
exp_setting.set_version(settings.DEV_VERSION)
# Show Version
version = exp_setting.get_version()
print('Version:', version.get_version())
# Get Cutoffs
cutoffs = Cutoffs()
cutoffs.query_cutoffs('95, 0, -5')
exp_setting.set_cutoffs(cutoffs)
print('Cutoffs data Initialized.')
# enable debugging mode
if args.enable_debug:
if set(args.enable_debug) & enable_debug:
#debug_mode = [1, 100000]
debug_mode = [1, 1000]
reduced_mode = True
if args.use_real_db:
if set(args.use_real_db) & choice_yes:
print('USING TEST DB: NO (USEING REAL/PRODUCTION DB)')
else:
settings.conn_string = settings.conn_string_test
print('USING TEST DB: YES')
if set(args.test_mode) & choice_yes:
exp_setting.set_test_mode(True)
print('TEST MODE: YES')
else:
exp_setting.set_test_mode(False)
print('TEST MODE: NO')
# ignore zero values
if set(args.ignore_zero) & choice_yes:
exp_setting.set_ignore_null(True)
print('Ignore zero values: YES')
else:
exp_setting.set_ignore_null(False)
print('Ignore zero values: NO')
# Gene info loading mode
if args.gene_load_mode:
if set(args.gene_load_mode) & gene_load_mode_pl:
exp_setting.set_gene_load_mode(settings.GN_LD_MODE_PL)
print('Gene loading mode: pre-load')
elif set(args.gene_load_mode) & gene_load_mode_dl:
exp_setting.set_gene_load_mode(settings.GN_LD_MODE_DL)
print('Gene loading mode: dynamic load')
# sequence type
if args.seq_type:
if set(args.seq_type) & seq_type_pep:
seq_type = 'p'
print('sequence type: amino acid (peptide)')
elif set(args.seq_type) & seq_type_dna:
seq_type = 'd'
print('sequence type: DNA')
elif set(args.seq_type) & seq_type_pmt:
seq_type = 'm1'
print('sequence type: Promoter data')
# set missing gnids in promoter data
exp_setting.set_missing_gnids_in_promoter()
elif set(args.seq_type) & seq_type_rda:
seq_type = 'p'
print('sequence type: Reduced Alphabet')
settings.RA_MODE = True
else: # default
seq_type = 'p'
print('sequence type: amino acid (peptide) - Default')
exp_setting.set_seq_type(seq_type)
# gp_type
gp_type = 'g'
if args.gp_type:
if set(args.gp_type) & gp_type_g:
gp_type = 'g'
print('gp type: g')
elif set(args.gp_type) & gp_type_p:
gp_type = 'p'
print('gp type: p')
elif set(args.gp_type) & gp_type_b:
gp_type = 'b'
print('gp type: b')
else:
gp_type = 'g'
print('gp type: g (default)')
exp_setting.set_gp_type(gp_type)
# assign feature groups
if args.feature_group:
if set(args.feature_group) & feature_group_gl:
print('new feature group: gene low expressed')
Features.gene_low_exp()
if set(args.feature_group) & feature_group_gh:
print('new feature group: gene high expressed, top 5%')
Features.gene_high_exp()
if set(args.feature_group) & feature_group_gh10:
print('new feature group: gene high expressed, top 10%')
Features.gene_high_exp_t10()
if set(args.feature_group) & feature_group_gt:
print('new feature group: gene for each tissue, top 10%')
Features.gene_tissues()
# feature set
if args.feature_set:
feature_set = args.feature_set
print('feature set: {}'.format(feature_set))
# set negative class mode
if args.neg_class_mode:
neg_class_mode = args.neg_class_mode
print('NEG_CLASS_MODE:', neg_class_mode)
if neg_class_mode in (settings.NEG_CLASS_MODE_NOT_P,
settings.NEG_CLASS_MODE_RND_S,
settings.NEG_CLASS_MODE_RND_M):
exp_setting.set_neg_class_mode(neg_class_mode)
else:
error_mesg = 'NEG_CLASS_MODE:', neg_class_mode, 'is UNKNOWN.'
raise ValueError(error_mesg)
# set percentile for new feature set
if args.percentile:
percentile_range = args.percentile.split(',')
percentile_range = [int(x)
for x in percentile_range] # str -> int type
print('Set percnetile range:', args.percentile)
# set gp combo configurations
feature_set_gp_comb = list()
if args.multi_gp:
multi_gp_conf = args.multi_gp
for conf in multi_gp_conf:
print(conf)
conf_list = conf.split(':')
feature_set_gp_comb.append(conf_list)
print(feature_set_gp_comb)
# class assignment for features
if args.features:
if set(args.features) & (settings.FN_GE_N | settings.FN_GE_B
| settings.FN_PA_N | settings.FN_PA_B):
if len(percentile_range) <= 0:
raise ValueError(
'percentile range is empty. Please set percentile range.')
'''
It supports adding multiple features at the same time, so it needs to do independently as belows.
'''
if set(args.features) & settings.FN_GE_N:
print('GE_N')
is_top = True
gp_type = 'g'
feature_set_name = next(iter(settings.FN_GE_N))
for percentile in range(percentile_range[0],
percentile_range[1],
percentile_range[2]):
add_feature_by_percentile(
gp_type=gp_type,
feature_set_name=feature_set_name,
percentile=percentile,
is_top=is_top)
if set(args.features) & settings.FN_GE_B:
print('GE_B')
is_top = False
gp_type = 'g'
feature_set_name = next(iter(settings.FN_GE_B))
for percentile in range(percentile_range[0],
percentile_range[1],
percentile_range[2]):
add_feature_by_percentile(
gp_type=gp_type,
feature_set_name=feature_set_name,
percentile=percentile,
is_top=is_top)
if set(args.features) & settings.FN_PA_N:
print('PA_N')
is_top = True
gp_type = 'p'
feature_set_name = next(iter(settings.FN_PA_N))
for percentile in range(percentile_range[0],
percentile_range[1],
percentile_range[2]):
add_feature_by_percentile(
gp_type=gp_type,
feature_set_name=feature_set_name,
percentile=percentile,
is_top=is_top)
if set(args.features) & settings.FN_PA_B:
print('PA_B')
is_top = False
gp_type = 'p'
feature_set_name = next(iter(settings.FN_PA_B))
for percentile in range(percentile_range[0],
percentile_range[1],
percentile_range[2]):
add_feature_by_percentile(
gp_type=gp_type,
feature_set_name=feature_set_name,
percentile=percentile,
is_top=is_top)
if set(args.features) & settings.FN_GPCB:
print('GE&PA Combination data')
for conf in feature_set_gp_comb:
add_feature_gp_comb(conf, exp_setting)
# build feature vector
if args.feature_vector:
intervals = [1000]
if set(args.feature_vector) & feature_vector:
print('build feature vector')
fs_set_idx = 0
#build_feature_vector()
if reduced_mode:
#for i in range(1,58938, interval):
for interval in intervals:
for i in range(1, 39324, interval):
#for i in range(16001,39324, interval):
debug_mode = [i, interval]
exp_setting.set_debug_mode(debug_mode)
build_feature_vector(exp_setting)
else:
for k in range(3, 8):
exp_setting.set_kmer_size(kmer_size=k)
exp_setting.set_genes_info(genes_info=None)
for fsid in feature_set:
# Version Info
print('Version:', settings.DEV_VERSION)
if fsid == 0:
# set feature info with dummy data for small assigned gene at random
fs_info = FeatureInfo(fsid=0,
fs_name='SM_RND',
gp_type='g',
class_size=2)
# Set assigned genes limit
assigned_genes_limit = [
int((x + 23 * fs_set_idx) * 10)
for x in range(1, 24)
]
exp_setting.set_assigned_genes_limit(
assigned_genes_limit)
fs_set_idx += 1
else:
# get feature set info from DB
res_fs_info = Pgsql.Common.select_data(
sqls.get_feature_set, (fsid))
fs_info = FeatureInfo(
fsid=fsid,
fs_name=res_fs_info[0][0].strip(),
gp_type=res_fs_info[0][1].strip(),
class_size=int(res_fs_info[0][2]))
exp_setting.set_fs_info(fs_info)
# for test
print(
'### MESSAGE ### fsid: {}, fs_name: {}, gp_type: {}, class_size: {}'
.format(exp_setting.get_fsid(),
exp_setting.get_fs_name(),
exp_setting.get_gp_type(),
exp_setting.get_class_size()))
debug_mode = [1, 0]
exp_setting.set_debug_mode(debug_mode)
build_feature_vector(exp_setting)
# single step classification
if args.validation_mode:
if set(args.validation_mode) & validation_mode_rg:
# reduced gene model
intervals = [1000, 2000, 3000, 4000, 5000]
print('validation - reduced genes model mode')
for interval in intervals:
for i in range(1, 39324, interval):
#for i in range(16001,39324, interval):
debug_mode = [i, interval]
exp_setting.set_debug_mode(debug_mode)
build_feature_vector(debug_mode=debug_mode,
gene_prot=gene_prot,
seq_type=seq_type)
