"""Generate random scores.
"""
import os
import logging
import numpy
import random
import click
from click.types import FLOAT
from bob.extension.scripts.click_helper import verbosity_option
import bob.core
from bob.io.base import create_directories_safe
from bob.measure.script import common_options
logger = logging.getLogger(__name__)
NUM_NEG = 5000
NUM_POS = 5000
[docs]def gen_score_distr(mean_neg, mean_pos, sigma_neg=10, sigma_pos=10):
"""Generate scores from normal distributions
Parameters
----------
mean_neg : float
Mean for negative scores
mean_pos : float
Mean for positive scores
sigma_neg : float
STDev for negative scores
sigma_pos : float
STDev for positive scores
Returns
-------
neg_scores : :any:`list`
Negatives scores
pos_scores : :any:`list`
Positive scores
"""
mt = bob.core.random.mt19937() # initialise the random number generator
neg_generator = bob.core.random.normal(numpy.float32, mean_neg, sigma_neg)
pos_generator = bob.core.random.normal(numpy.float32, mean_pos, sigma_pos)
neg_scores = [neg_generator(mt) for _ in range(NUM_NEG)]
pos_scores = [pos_generator(mt) for _ in range(NUM_NEG)]
return neg_scores, pos_scores
[docs]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
----------
neg : :py:class:`numpy.ndarray`
Scores for negative samples.
pos : :py:class:`numpy.ndarray`
Scores for positive samples.
filename : str
The path to write the score to.
n_sys : int
Number of different systems
five_col : bool
If 5-colum format, else 4-column
"""
create_directories_safe(os.path.dirname(filename))
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_subjects)
s_five = ' ' if not five_col else ' d' + \
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_names = random.sample(s_subjects, 2)
s_five = ' ' if not five_col else ' d' + \
random.choice(s_names) + ' '
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,\
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_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, 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_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_subjects, n_probes_per_subjects,
n_unknown_subjects, neg_unknown, five_col)