Implemented multiple subjects score generation and unknown probes

bob/bio/base/script/gen.py

@@ -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)
+