Fix split management + black formatting

postprocessing/borda_global_juxt_rSNF.py

@@ -37,7 +37,9 @@ parser.add_argument('--datafolder', type=str, help='Main data folder')
 parser.add_argument('--outfolder', type=str, help='Results folder')
 parser.add_argument('--dataset', type=str, help='Dataset name')
 parser.add_argument('--target', type=str, help='Clinical endpoint')
-parser.add_argument('--model', type=str, help='Classifiers implemented, randomForest or LSVM')
+parser.add_argument(
+    '--model', type=str, help='Classifiers implemented, randomForest or LSVM'
+)
 parser.add_argument('--layers', type=str, nargs='+', help='')
 parser.add_argument('--n_splits_end', type=int, help='')
 parser.add_argument('--n_splits_start', type=int, help='')
@@ -65,15 +67,31 @@ for k in range(2, N_LAYERS + 1):
     for comb in combinations(LAYERS, k):
 
         layers_concat = '_'.join(comb)
-        _, var_names, _ = load_data(os.path.join(DATAFOLDER, DATASET, TARGET, f'0/{layers_concat}_tr.txt') )
-        rankings = [] 
-
-        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')    
+        _, var_names, _ = load_data(
+            os.path.join(
+                DATAFOLDER, DATASET, TARGET, f'{N_SPLITS_START}/{layers_concat}_tr.txt'
+            )
+        )
+        rankings = []
+
+        for split_id in range(N_SPLITS_START, N_SPLITS_END):
+
+            if MODE == 'rSNF':
+                file_ranking = os.path.join(
+                    OUTFOLDER,
+                    DATASET,
+                    TARGET,
+                    MODEL,
+                    f'{split_id}/{MODE}/{layers_concat}_tr_{MODEL}_rankList_ranking.csv.gz',
+                )
             else:
-                file_ranking = os.path.join(OUTFOLDER, DATASET, TARGET, MODEL, f'{i}/{MODE}/{layers_concat}_tr_{MODEL}_KBest_ranking.csv.gz')  
+                file_ranking = os.path.join(
+                    OUTFOLDER,
+                    DATASET,
+                    TARGET,
+                    MODEL,
+                    f'{split_id}/{MODE}/{layers_concat}_tr_{MODEL}_KBest_ranking.csv.gz',
+                )
 
             rank = pd.read_csv(file_ranking, header=None, sep='\t').values
             rankings.append(rank)
@@ -83,6 +101,14 @@ for k in range(2, N_LAYERS + 1):
         borda_df = pd.DataFrame(columns=["FEATURE_ID", "FEATURE_NAME", "MEAN_POS"])
 
         for i, pos in zip(BORDA_ID, BORDA_POS):
-            borda_df = borda_df.append({'FEATURE_ID': i, 'FEATURE_NAME': var_names[i], 'MEAN_POS': pos+1}, ignore_index=True)
-
-        borda_df.to_csv(f"{OUTFOLDER}/{DATASET}/{TARGET}/{MODEL}/Borda_splits_{N_SPLITS_START}-{N_SPLITS_END}_{MODE}_{layers_concat}.txt", sep='\t', index=False, float_format="%.3f")
+            borda_df = borda_df.append(
+                {'FEATURE_ID': i, 'FEATURE_NAME': var_names[i], 'MEAN_POS': pos + 1},
+                ignore_index=True,
+            )
+
+        borda_df.to_csv(
+            f"{OUTFOLDER}/{DATASET}/{TARGET}/{MODEL}/Borda_splits_{N_SPLITS_START}-{N_SPLITS_END}_{MODE}_{layers_concat}.txt",
+            sep='\t',
+            index=False,
+            float_format="%.3f",
+        )

postprocessing/borda_global_rSNFi.py

 #%%
+import argparse
+import operator
 import os
+from itertools import combinations
+
 import numpy as np
 import pandas as pd
-import operator
-import argparse
-from itertools import combinations
+
 
 #%%
 class myArgumentParser(argparse.ArgumentParser):
@@ -27,7 +29,9 @@ parser.add_argument('--datafolder', type=str, help='Main data folder')
 parser.add_argument('--outfolder', type=str, help='Results folder')
 parser.add_argument('--dataset', type=str, help='Dataset name')
 parser.add_argument('--target', type=str, help='Clinical endpoint')
-parser.add_argument('--model', type=str, help='Classifiers implemented, randomForest or LSVM')
+parser.add_argument(
+    '--model', type=str, help='Classifiers implemented, randomForest or LSVM'
+)
 parser.add_argument('--layers', type=str, nargs='+', help='')
 parser.add_argument('--n_splits_end', type=int, help='')
 parser.add_argument('--n_splits_start', type=int, help='')
@@ -52,17 +56,23 @@ N_LAYERS = len(LAYERS)
 CV_K = 5
 CV_N = 10
 
-#%% 
+#%%
 for k in range(2, N_LAYERS + 1):
 
     for comb in combinations(LAYERS, k):
 
         layers_concat = '_'.join(comb)
 
-        all_feats=[]
+        all_feats = []
 
-        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') 
+        for i in range(N_SPLITS_START, N_SPLITS_END):
+            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))
 
@@ -70,32 +80,51 @@ for k in range(2, N_LAYERS + 1):
 
         positions = dict()
         means = dict()
-        x=((len(all_feats)-1)*np.ones((1,(N_SPLITS_END-N_SPLITS_START)*CV_K*CV_N)))
+        x = (len(all_feats) - 1) * np.ones(
+            (1, (N_SPLITS_END - N_SPLITS_START) * CV_K * CV_N)
+        )
 
         for i in all_feats:
-            positions[i]=x.tolist()[0]
-            means[i]=0.0
-
-        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') 
+            positions[i] = x.tolist()[0]
+            means[i] = 0.0
+
+        for i, split_id in enumerate(range(N_SPLITS_START, N_SPLITS_END)):
+
+            file_featureList = os.path.join(
+                OUTFOLDER,
+                DATASET,
+                TARGET,
+                MODEL,
+                f'{split_id}/{MODE}/{layers_concat}_tr_{MODEL}_KBest_featurelist.txt',
+            )
             feats = pd.read_csv(file_featureList, sep='\t')
-            
-            z=[None]*len(feats)
 
-            for k in range(len(feats)):
-                z[feats.FEATURE_ID[k]]=feats.FEATURE_NAME[k]
+            z = [None] * len(feats)
 
-            file_ranking = os.path.join(OUTFOLDER, DATASET, TARGET, MODEL, f'{i}/{MODE}/{layers_concat}_tr_{MODEL}_KBest_ranking.csv.gz') 
+            for k in range(len(feats)):
+                z[feats.FEATURE_ID[k]] = feats.FEATURE_NAME[k]
+
+            file_ranking = os.path.join(
+                OUTFOLDER,
+                DATASET,
+                TARGET,
+                MODEL,
+                f'{split_id}/{MODE}/{layers_concat}_tr_{MODEL}_KBest_ranking.csv.gz',
+            )
             rankings = pd.read_csv(file_ranking, header=None, sep='\t')
-            for j in range(CV_K*CV_N):
+            for j in range(CV_K * CV_N):
                 for k in range(rankings.shape[1]):
-                    positions[z[rankings.iloc[j][k]]][i*(CV_K*CV_N)+j]=1.0*k
+                    positions[z[rankings.iloc[j][k]]][i * (CV_K * CV_N) + j] = 1.0 * k
 
         for i in all_feats:
-            means[i]=np.mean(positions[i])
+            means[i] = np.mean(positions[i])
 
         sorted_means = sorted(means.items(), key=operator.itemgetter(1))
-        
+
         borda_df = pd.DataFrame(sorted_means, columns=['FEATURE_NAME', 'MEAN_POS'])
-        borda_df.to_csv(f"{OUTFOLDER}/{DATASET}/{TARGET}/{MODEL}/Borda_splits_{N_SPLITS_START}-{N_SPLITS_END}_{MODE}_{layers_concat}.txt", sep='\t', index=False, float_format="%.3f")
\ No newline at end of file
+        borda_df.to_csv(
+            f"{OUTFOLDER}/{DATASET}/{TARGET}/{MODEL}/Borda_splits_{N_SPLITS_START}-{N_SPLITS_END}_{MODE}_{layers_concat}.txt",
+            sep='\t',
+            index=False,
+            float_format="%.3f",
+        )