Ajout boucle résultats concis + modification arg split

main.py

@@ -135,7 +135,7 @@ if __name__ == "__main__":
     #                                                 Split data
     # =================================================================================================================
 
-        if args.split:
+        if args.split and args.task in ["train", "all"]:
             splitter = BaseSplitter(f"{INPUT_DIR}/{config_name}")
             splitter.split_data()
 

results/roc.py

@@ -42,9 +42,9 @@ class ROCResults(BaseResults):
                 fpr, tpr, _ = roc_curve(labels, y_pred)
 
                 if algo in self.result:
-                    self.result[algo][config_name] = {"classic": round(score, 2)}
+                    self.result[algo][config_name] = {"classic": round(score, 4)}
                 else:
-                    self.result[algo] = {config_name: {"classic": round(score, 2)}}
+                    self.result[algo] = {config_name: {"classic": round(score, 4)}}
 
                 plt.rcParams["figure.figsize"] = (10, 10)
                 plt.plot(fpr, tpr)
@@ -71,7 +71,7 @@ class ROCResults(BaseResults):
                         result = np.maximum(result, y_pred)
 
                     score = roc_auc_score(labels, result)
-                    self.result[algo][f"{config_name}"]["auto_split"] = round(score, 2)
+                    self.result[algo][f"{config_name}"]["auto_split"] = round(score, 4)
 
                 # Compute results for splitted dataset
                 files = __exec(f"find -L {self.path}/{config_name}/results_{algo} -regex '^.*dataset[_0-9]+_{algo}.ts'")
@@ -87,10 +87,24 @@ class ROCResults(BaseResults):
                     result = np.maximum(result, y_pred)
 
                 score = roc_auc_score(labels, result)
-                self.result[algo][f"{config_name}"]["split"] = round(score, 2)
+                self.result[algo][f"{config_name}"]["split"] = round(score, 4)
 
         print(json.dumps(self.result))
 
+        # for algo, algo_res in self.result.items():
+        #     l_classic = []
+        #     l_auto_split = []
+        #     l_split = []
+        #     for details in algo_res.values():
+        #         l_classic.append(details["classic"])
+        #         l_auto_split.append(details["auto_split"])
+        #         l_split.append(details["split"])
+        #     print(f"{algo}:")
+        #     print(f"\tClassic: {np.mean(l_classic)} ({np.std(l_classic)})")
+        #     print(f"\tAuto split: {np.mean(l_auto_split)} ({np.std(l_auto_split)})")
+        #     print(f"\tSplit: {np.mean(l_split)} ({np.std(l_split)})")
+
+
     def __vote_for_score(self, scores, length):
         """
         Compute the score for each point of the dataset instead of a per window basis.