def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
input_data = opts.input_data
mapping_file = opts.mapping_file
category = opts.category
ntree = opts.ntree
errortype = opts.errortype
output_dir = opts.output_dir
verbose = opts.verbose
force = opts.force
collate_results_fp = opts.collate_results_fp
# create the output directories
try:
makedirs(opts.output_dir)
except OSError:
if force:
pass
else:
# This check helps users avoid overwriting previous output.
option_parser.error(
"Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
# verify that category is in mapping file
map_list = parse_mapping_file(open(mapping_file, 'U').readlines())
if not category in map_list[1][1:]:
option_parser.error(
"Category '%s' not found in mapping file columns:" % (category))
print map_list[1][1:]
exit(1)
# if input is a single otu table
if isdir(input_data) is False:
# run the supervised learning algorithm
result = run_supervised_learning(input_data, mapping_file, category,
ntree, errortype, output_dir, verbose)
# if input is a directory of otu tables
if isdir(input_data) is True:
input_tables = glob('%s/*biom' % input_data)
coll_est_error = []
coll_est_error_stdev = []
baseline_error = []
for table_fp in input_tables:
# create output dir on per-table basis with convention:
# "sl_TABLENAME_CATEGORY/"
output_basename = table_fp.split('/')[-1]
output_basename = output_basename.replace('.biom', '')
output_name = "sl_%s_%s/" % (output_basename, category)
output_fp = join(output_dir, output_name)
# create the output directories
try:
makedirs(output_fp)
except OSError:
if force:
pass
else:
# This check helps users avoid overwriting previous output.
option_parser.error(
"Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
result = run_supervised_learning(table_fp, mapping_file, category,
ntree, errortype, output_fp,
verbose)
# retrieve the estimated error and baseline error
est_error_line, baseline_error_line = \
result['summary'].readlines()[2:4]
est_error_line = est_error_line.split('\t')[1]
coll_est_error.append(float(est_error_line.split(' ')[0]))
# only collect standard deviations for cv5 and cv10 errortypes
if errortype in ['cv5', 'cv10']:
est_error_stdev = est_error_line.split(' ')[2].strip()
coll_est_error_stdev.append(float(est_error_stdev))
# make sure baseline error is the same across all tables (it should be)
if baseline_error == []:
baseline_error.append(
float(baseline_error_line.split('\t')[1].strip()))
if collate_results_fp:
output_file = open(collate_results_fp, 'w')
# get assembled results
results = assemble_results(coll_est_error, coll_est_error_stdev,
baseline_error[0], errortype, ntree)
output_file.write('\n'.join(results))
output_file.close()
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
input_data = opts.input_data
mapping_file = opts.mapping_file
category = opts.category
ntree = opts.ntree
errortype = opts.errortype
output_dir = opts.output_dir
verbose = opts.verbose
force = opts.force
collate_results_fp = opts.collate_results_fp
# create the output directories
try:
makedirs(opts.output_dir)
except OSError:
if force:
pass
else:
# This check helps users avoid overwriting previous output.
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
# verify that category is in mapping file
map_list = parse_mapping_file(open(mapping_file, 'U').readlines())
if not category in map_list[1][1:]:
option_parser.error(
"Category '%s' not found in mapping file columns:" %
(category))
print map_list[1][1:]
exit(1)
# if input is a single otu table
if isdir(input_data) is False:
# run the supervised learning algorithm
result = run_supervised_learning(input_data, mapping_file, category,
ntree, errortype, output_dir, verbose)
# if input is a directory of otu tables
if isdir(input_data) is True:
input_tables = glob('%s/*biom' % input_data)
coll_est_error = []
coll_est_error_stdev = []
baseline_error = []
for table_fp in input_tables:
# create output dir on per-table basis with convention:
# "sl_TABLENAME_CATEGORY/"
output_basename = table_fp.split('/')[-1]
output_basename = output_basename.replace('.biom', '')
output_name = "sl_%s_%s/" % (output_basename, category)
output_fp = join(output_dir, output_name)
# create the output directories
try:
makedirs(output_fp)
except OSError:
if force:
pass
else:
# This check helps users avoid overwriting previous output.
option_parser.error("Output directory already exists. Please choose"
" a different directory, or force overwrite with -f.")
result = run_supervised_learning(table_fp, mapping_file, category,
ntree, errortype, output_fp, verbose)
# retrieve the estimated error and baseline error
est_error_line, baseline_error_line = \
result['summary'].readlines()[2:4]
est_error_line = est_error_line.split('\t')[1]
coll_est_error.append(float(est_error_line.split(' ')[0]))
# only collect standard deviations for cv5 and cv10 errortypes
if errortype in ['cv5', 'cv10']:
est_error_stdev = est_error_line.split(' ')[2].strip()
coll_est_error_stdev.append(float(est_error_stdev))
# make sure baseline error is the same across all tables (it should
# be)
if baseline_error == []:
baseline_error.append(
float(baseline_error_line.split('\t')[1].strip()))
if collate_results_fp:
output_file = open(collate_results_fp, 'w')
# get assembled results
results = assemble_results(coll_est_error, coll_est_error_stdev,
baseline_error[0], errortype, ntree)
output_file.write('\n'.join(results))
output_file.close()