manage custom splits

postprocessing/borda_global_juxt_rSNF.py

@@ -68,7 +68,7 @@ for k in range(2, N_LAYERS + 1):
         _, var_names, _ = load_data(os.path.join(DATAFOLDER, DATASET, TARGET, f'0/{layers_concat}_tr.txt') )
         rankings = [] 
 
-        for i in range(N_SPLITS_END-N_SPLITS_START):
+        for i in range(N_SPLITS_START, N_SPLITS_END-N_SPLITS_START):
 
             if MODE == 'rSNF':  
                 file_ranking = os.path.join(OUTFOLDER, DATASET, TARGET, MODEL, f'{i}/{MODE}/{layers_concat}_tr_{MODEL}_rankList_ranking.csv.gz')    

postprocessing/borda_global_rSNFi.py

@@ -61,7 +61,7 @@ for k in range(2, N_LAYERS + 1):
 
         all_feats=[]
 
-        for i in range(N_SPLITS_END-N_SPLITS_START):
+        for i in range(N_SPLITS_START, N_SPLITS_END-N_SPLITS_START):
             file_featureList = os.path.join(OUTFOLDER, DATASET, TARGET, MODEL, f'{i}/{MODE}/{layers_concat}_tr_{MODEL}_KBest_featurelist.txt') 
             feats = pd.read_csv(file_featureList, sep='\t')
             all_feats.extend(list(feats.FEATURE_NAME))
@@ -76,7 +76,7 @@ for k in range(2, N_LAYERS + 1):
             positions[i]=x.tolist()[0]
             means[i]=0.0
 
-        for i in range(N_SPLITS_END-N_SPLITS_START):
+        for i in range(N_SPLITS_START, N_SPLITS_END-N_SPLITS_START):
 
             file_featureList = os.path.join(OUTFOLDER, DATASET, TARGET, MODEL, f'{i}/{MODE}/{layers_concat}_tr_{MODEL}_KBest_featurelist.txt') 
             feats = pd.read_csv(file_featureList, sep='\t')

postprocessing/compute_all_metrics.py

@@ -100,7 +100,7 @@ for k in range_combinations:
         all_test_mccs = []
         best_feat_steps = []
 
-        for split_id in range(N_SPLITS_END-N_SPLITS_START):
+        for split_id in range(N_SPLITS_START, N_SPLITS_END-N_SPLITS_START):
 
             PATH = f'{OUTFOLDER}/{DATASET}/{TARGET}/{MODEL}/{split_id}'