Commit 5c0dfe6f authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira

Merge branch 'fix-bio-bio-gen' into 'master'

Implemented multiple subjects score generation, unknown probes and fixed issues

See merge request !171
parents 884bc7c5 d1042b3c
Pipeline #23098 passed with stages
in 37 minutes and 57 seconds
......@@ -49,7 +49,8 @@ def gen_score_distr(mean_neg, mean_pos, sigma_neg=10, sigma_pos=10):
return neg_scores, pos_scores
def write_scores_to_file(neg, pos, filename, n_sys=1, five_col=False):
def write_scores_to_file(neg, pos, filename, n_subjects=5, n_probes_per_subject=5,
n_unknown_subjects=0, neg_unknown=None, five_col=False):
""" Writes score distributions
Parameters
......@@ -66,39 +67,76 @@ def write_scores_to_file(neg, pos, filename, n_sys=1, five_col=False):
If 5-colum format, else 4-column
"""
create_directories_safe(os.path.dirname(filename))
s_names = ['s%d' % i for i in range(n_sys)]
s_subjects = ['x%d' % i for i in range(n_subjects)]
with open(filename, 'wt') as f:
for i in pos:
s_name = random.choice(s_names)
s_name = random.choice(s_subjects)
s_five = ' ' if not five_col else ' d' + \
random.choice(s_names) + ' '
f.write('x%sx %s %f\n' % (s_five, s_name, i))
random.choice(s_subjects) + ' '
probe_id = "%s_%d" %(s_name, random.randint(0, n_probes_per_subject-1))
f.write('%s%s%s %s %f\n' % (s_name, s_five, s_name, probe_id, i))
for i in neg:
s_name = random.choice(s_names)
s_names = random.sample(s_subjects, 2)
s_five = ' ' if not five_col else ' d' + \
random.choice(s_names) + ' '
f.write('x%sy %s %f\n' % (s_five, s_name, i))
probe_id = "%s_%d" %(s_names[1], random.randint(0, n_probes_per_subject-1))
f.write('%s%s%s %s %f\n' % (s_names[0], s_five, s_names[1], probe_id, i))
if neg_unknown is not None:
s_unknown_subjects = ['u%d' % i for i in range(n_unknown_subjects)]
for i in neg_unknown:
s_name = random.choice(s_subjects)
s_name_probe = random.choice(s_unknown_subjects)
s_five = ' ' if not five_col else ' d' + \
random.choice(s_subjects) + ' '
probe_id = "%s_%d" %(s_name_probe, random.randint(0, n_probes_per_subject-1))
f.write('%s%s%s %s %f\n' % (s_name, s_five, s_name_probe, probe_id, i))
@click.command()
@click.argument('outdir')
@click.option('-mm', '--mean-match', default=10, type=FLOAT, show_default=True)
@click.option('-mnm', '--mean-non-match', default=-10, type=FLOAT, show_default=True)
@click.option('-n', '--n-sys', default=1, type=click.INT, show_default=True)
@click.option('-mm', '--mean-match', default=10, type=FLOAT, show_default=True,\
help="Mean for the positive scores distribution")
@click.option('-mnm', '--mean-non-match', default=-10, type=FLOAT, show_default=True,\
help="Mean for the negative scores distribution")
@click.option('-p', '--n-probes-per-subjects', default=5, type=click.INT, show_default=True,\
help="Number of probes per subject")
@click.option('-s', '--n-subjects', default=5, type=click.INT, show_default=True,\
help="Number of subjects")
@click.option('-p', '--sigma-positive', default=10, type=click.FLOAT, show_default=True,\
help="Variance for the positive score distributions")
@click.option('-n', '--sigma-negative', default=10, type=click.FLOAT, show_default=True,\
help="Variance for the negative score distributions")
@click.option('-u', '--n-unknown-subjects', default=0, type=click.INT, show_default=True,\
help="Number of unknown subjects (useful for openset plots)")
@click.option('--five-col/--four-col', default=False, show_default=True)
@verbosity_option()
def gen(outdir, mean_match, mean_non_match, n_sys, five_col, **kwargs):
def gen(outdir, mean_match, mean_non_match, n_probes_per_subjects, n_subjects,\
sigma_positive, sigma_negative, n_unknown_subjects, five_col, **kwargs):
"""Generate random scores.
Generates random scores in 4col or 5col format. The scores are generated
using Gaussian distribution whose mean is an input
parameter. The generated scores can be used as hypothetical datasets.
"""
# Generate the data
neg_dev, pos_dev = gen_score_distr(mean_non_match, mean_match)
neg_eval, pos_eval = gen_score_distr(mean_non_match, mean_match)
neg_dev, pos_dev = gen_score_distr(mean_non_match, mean_match, sigma_negative, sigma_positive)
neg_eval, pos_eval = gen_score_distr(mean_non_match, mean_match, sigma_negative, sigma_positive)
# For simplicity I will use the same distribution for dev-eval
if n_unknown_subjects:
neg_unknown,_ = gen_score_distr(mean_non_match, mean_match, sigma_negative, sigma_positive)
else:
neg_unknown = None
# Write the data into files
write_scores_to_file(neg_dev, pos_dev,
os.path.join(outdir, 'scores-dev'), n_sys, five_col)
os.path.join(outdir, 'scores-dev'),
n_subjects, n_probes_per_subjects,
n_unknown_subjects, neg_unknown, five_col)
write_scores_to_file(neg_eval, pos_eval,
os.path.join(outdir, 'scores-eval'), n_sys, five_col)
os.path.join(outdir, 'scores-eval'),
n_subjects, n_probes_per_subjects,
n_unknown_subjects, neg_unknown, five_col)
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