os.path.isfile('{}/{}.{}'.format(trg_dir, trainingset_sample_indices_file, trainingset_suffix)):
if override:
print 'WARNING: The target directory {}/ already contains at least on of the files to create. Replacing.'.format(trg_dir)
else:
print 'WARNING: The target directory {}/ already contains at least on of the files to create. Skipping.'.format(trg_dir)
sys.exit(1)
# initializing collections
training_set_selections = dict.fromkeys(training_set_cases)
# iterate over cases, load their respective samples and perform a sampling for each
# draw random stratified sample and extract training set indices
sss = StratifiedShuffleSplit(classes, n_iter=1, train_size=n_samples)
sample_indices, _ = sss.next()
# save
def load_feature_struct(f):
"Load the feature struct from a feature config file."
d, m = os.path.split(os.path.splitext(f)[0])
f, filename, desc = imp.find_module(m, [d])
return imp.load_module(m, f, filename, desc).features_to_extract
def load_feature_names(f):
"Load the feature names from a feature config file."
fs = load_feature_struct(f)
return [feature_struct_entry_to_name(e) for e in fs]
if __name__ == "__main__":
