Implemented multiple subjects score generation and unknown probes

parent 884bc7c5
Pipeline #23081 passed with stage
in 13 minutes and 54 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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