modifié : AugmentTests.py

AugmentTests.py

@@ -7,26 +7,27 @@ if __name__ == "__main__":
     args = load_args()
     
     #On commence avec le standard
-    losses = np.zeros(20); accs = np.zeros(20)
-    for random in range(20):
-        args.random_state = random
-        losses[random], accs[random] = run_duo(args)
-    records = pd.DataFrame([["Standard",losses.mean(),losses.std(),accs.mean(),accs.std()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])
-    records.to_csv("output/perfs.csv",index = False)
+    # losses = np.zeros(20); accs = np.zeros(20)
+    # for random in range(20):
+    #     args.random_state = random
+    #     losses[random], accs[random] = run_duo(args)
+    # records = pd.DataFrame([["Standard",losses.mean(),losses.std(),accs.mean(),accs.std()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])
+    # records.to_csv("output/perfs.csv",index = False)
         
 
-    # #Et enfin le rt-shift
-    # for prob in [k/20 for k in range(1,21)]:
-    #     args.augment_args[2] = prob
-    #     for mean in [5,10,15,20,25,30,40,50,60,70,80,90]:
-    #         args.augment_args[5] = mean
-    #         for std in [mean/k for k in range(1,11)]:
-    #             args.augment_args[6] = std
-    #             losses = np.zeros(5); accs = np.zeros(5)
-    #             for random in range(5):
-    #                 args.random_state = random
-    #                 losses[random], accs[random] = run_duo(args)
-    #             records = pd.concat([records,pd.DataFrame([[f"rtShift prob{prob} mean{mean} std{std}",losses.mean(),losses.std(),accs.mean(),accs.std()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])])
-    #             records.to_csv("output/DataAugment/perfs.csv",index = False)
-            
+    #Et enfin le rt-shift
+    records = pd.read_csv("output/perfs.csv")
+    for prob in [k/5 for k in range(1,6)]:
+        args.augment_args[2] = prob
+        mean = 0
+        args.augment_args[5] = mean
+        for std in [k/2 for k in range(5,25,5)]:
+            args.augment_args[6] = std
+            losses = np.zeros(5); accs = np.zeros(5)
+            for random in range(5):
+                args.random_state = random
+                losses[random], accs[random] = run_duo(args)
+            records = pd.concat([records,pd.DataFrame([[f"rtShift prob{prob} mean{mean} std{std}",losses.mean().item(),losses.std().item(),accs.mean().item(),accs.std().item()]],columns = ["Augmentation","mu_loss","std_loss","mu_acc","std_acc"])])
+            records.to_csv("output/DataAugment/perfs.csv",index = False)
+        
     
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