updates

eDiscovery/main.py

@@ -91,6 +91,8 @@ def main(args):
                     tee(f'{i}\t{progress:.2f}\t{nDtr}\t{nDte}\t{tr_p[1]:.3f}\t{te_p[1]:.3f}\t{pool_p_hat_q[1]:.3f}\t{pool_p_hat_cc[1]:.3f}'
                         f'\t{r:.3f}\t{r_hat_q:.3f}\t{r_hat_cc:.3f}\t{tr_te_shift:.5f}\t{ae_q:.4f}\t{ae_cc:.4f}\t{f1_q:.3f}\t{f1_clf:.3f}')
 
+                    raise Exception('add idealized costs for each iteration and plots')
+
                     posteriors = classifier.predict_proba(pool.instances)
                     fig.plot(posteriors, pool.labels)
 
@@ -120,10 +122,10 @@ if __name__ == '__main__':
     parser.add_argument('--k', metavar='BATCH', type=int, help='number of documents in a batch',
                         default=100)
     parser.add_argument('--initsize', metavar='SIZE', type=int, help='number of labelled documents at the beginning',
-                        default=1000)
+                        default=2)
     parser.add_argument('--initprev', metavar='PREV', type=float,
                         help='prevalence of the initial sample (-1 for uniform sampling, default)',
-                        default=-1)
+                        default=0.5)
     parser.add_argument('--seed', metavar='SEED', type=int,
                         help='random seed',
                         default=1)