def setUp(self):
# Temporary input file
fd, self.tmp_otu_filepath = mkstemp(prefix='R_test_otu_table_',
suffix='.txt')
close(fd)
seq_file = open(self.tmp_otu_filepath, 'w')
seq_file.write(test_otu_table)
seq_file.close()
fd, self.tmp_map_filepath = mkstemp(prefix='R_test_map_',
suffix='.txt')
close(fd)
seq_file = open(self.tmp_map_filepath, 'w')
seq_file.write(test_map)
seq_file.close()
self.files_to_remove = \
[self.tmp_otu_filepath, self.tmp_map_filepath]
# Prep input files in R format
output_dir = mkdtemp()
self.dirs_to_remove = [output_dir]
# get random forests results
mkdir(join(output_dir, 'random_forest'))
self.results = run_supervised_learning(
self.tmp_otu_filepath, self.tmp_map_filepath, 'Individual',
ntree=100, errortype='oob',
output_dir=output_dir)
def setUp(self):
# Temporary input file
self.tmp_otu_filepath = get_tmp_filename(
prefix='R_test_otu_table_',
suffix='.txt'
)
seq_file = open(self.tmp_otu_filepath, 'w')
seq_file.write(test_otu_table)
seq_file.close()
self.tmp_map_filepath = get_tmp_filename(
prefix='R_test_map_',
suffix='.txt'
)
seq_file = open(self.tmp_map_filepath, 'w')
seq_file.write(test_map)
seq_file.close()
self.files_to_remove = \
[self.tmp_otu_filepath, self.tmp_map_filepath]
# Prep input files in R format
output_dir = mkdtemp()
self.dirs_to_remove = [output_dir]
# get random forests results
mkdir(join(output_dir, 'random_forest'))
self.results = run_supervised_learning(
self.tmp_otu_filepath, self.tmp_map_filepath,'Individual',
ntree=100, errortype='oob',
output_dir=output_dir)
def setUp(self):
# Temporary input file
self.tmp_otu_filepath = get_tmp_filename(prefix="R_test_otu_table_", suffix=".txt")
seq_file = open(self.tmp_otu_filepath, "w")
seq_file.write(test_otu_table)
seq_file.close()
self.tmp_map_filepath = get_tmp_filename(prefix="R_test_map_", suffix=".txt")
seq_file = open(self.tmp_map_filepath, "w")
seq_file.write(test_map)
seq_file.close()
self.files_to_remove = [self.tmp_otu_filepath, self.tmp_map_filepath]
# Prep input files in R format
output_dir = mkdtemp()
self.dirs_to_remove = [output_dir]
# get random forests results
mkdir(join(output_dir, "random_forest"))
self.results = run_supervised_learning(
self.tmp_otu_filepath,
self.tmp_map_filepath,
"Individual",
ntree=100,
errortype="oob",
output_dir=output_dir,
)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
# create the output directories
try:
makedirs(opts.output_dir)
except OSError:
if opts.force:
pass
else:
# This check helps users avoid overwriting previous output.
print "Output directory already exists. Please choose "+\
"a different directory, or force overwrite with -f."
exit(1)
# verify that category is in mapping file
map_list = parse_mapping_file(open(opts.mapping_file, 'U').readlines())
if not opts.category in map_list[1][1:]:
print "Category '%s' not found in mapping file columns:" % (
opts.category)
print map_list[1][1:]
exit(1)
# run the supervised learning algorithm
result = run_supervised_learning(opts.input_data,
opts.mapping_file,
opts.category,
ntree=opts.ntree,
errortype=opts.errortype,
output_dir=opts.output_dir,
verbose=opts.verbose)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
# create the output directories
try:
makedirs(opts.output_dir)
except OSError:
if opts.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(opts.mapping_file,'U').readlines())
if not opts.category in map_list[1][1:]:
print "Category '%s' not found in mapping file columns:" %(opts.category)
print map_list[1][1:]
exit(1)
# run the supervised learning algorithm
result = run_supervised_learning(opts.input_data, opts.mapping_file, opts.category,
ntree=opts.ntree, errortype=opts.errortype,
output_dir=opts.output_dir, verbose=opts.verbose)
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()