Add PPV (precision)

postprocessing/compute_all_metrics.py

@@ -62,6 +62,9 @@ df_results = pd.DataFrame(
         'spec_train',
         'spec_train_min',
         'spec_train_max',
+        'ppv_train',
+        'ppv_train_min',
+        'ppv_train_max',
         'mcc_test',
         'mcc_test_min',
         'mcc_test_max',
@@ -83,6 +86,7 @@ for k in range_combinations:
         all_mccs = []
         all_sens = []
         all_spec = []
+        all_ppv = []
         all_aucs = []
 
         all_test_mccs = []
@@ -164,6 +168,10 @@ for k in range_combinations:
                 best_spec = all_metrics.iloc[best_idxs, SPEC]
                 all_spec.extend(best_spec)
 
+                PPV = np.where(all_metrics.columns == 'ppv')[0][0]
+                best_ppv = all_metrics.iloc[best_idxs, PPV]
+                all_ppv.extend(best_ppv)
+
         all_mccs = np.array(all_mccs)
         MCC_CI = bs.bootstrap(all_mccs, stat_func=bs_stats.mean)
         print(
@@ -194,8 +202,10 @@ for k in range_combinations:
         if TARGET != 'subtypes':
             all_sens = np.array(all_sens)
             all_spec = np.array(all_spec)
+            all_ppv = np.array(all_ppv)
             SENS_CI = bs.bootstrap(all_sens, stat_func=bs_stats.mean)
             SPEC_CI = bs.bootstrap(all_spec, stat_func=bs_stats.mean)
+            PPV_CI = bs.bootstrap(all_ppv, stat_func=bs_stats.mean)
             print(
                 'SENS =',
                 round(np.mean(all_sens), 3),
@@ -206,6 +216,11 @@ for k in range_combinations:
                 round(np.mean(all_spec), 3),
                 (round(SPEC_CI.lower_bound, 3), round(SPEC_CI.upper_bound, 3)),
             )
+            print(
+                'PPV =',
+                round(np.mean(all_ppv), 3),
+                (round(PPV_CI.lower_bound, 3), round(PPV_CI.upper_bound, 3)),
+            )
 
             row = OrderedDict(
                 {
@@ -222,6 +237,9 @@ for k in range_combinations:
                     'spec_train': round(np.mean(all_spec), 3),
                     'spec_train_min': round(SPEC_CI.lower_bound, 3),
                     'spec_train_max': round(SPEC_CI.upper_bound, 3),
+                    'ppv_train': round(np.mean(all_ppv), 3),
+                    'ppv_train_min': round(PPV_CI.lower_bound, 3),
+                    'ppv_train_max': round(PPV_CI.upper_bound, 3),
                     'mcc_test': round(np.mean(all_test_mccs), 3),
                     'mcc_test_min': round(MCC_TEST.lower_bound, 3),
                     'mcc_test_max': round(MCC_TEST.upper_bound, 3),
@@ -246,6 +264,9 @@ for k in range_combinations:
                     'spec_train': np.nan,
                     'spec_train_min': np.nan,
                     'spec_train_max': np.nan,
+                    'ppv_train': np.nan,
+                    'ppv_train_min': np.nan,
+                    'ppv_train_max': np.nan,                    
                     'mcc_test': round(np.mean(all_test_mccs), 3),
                     'mcc_test_min': round(MCC_TEST.lower_bound, 3),
                     'mcc_test_max': round(MCC_TEST.upper_bound, 3),