prod version

Author: DodoEstinto

.gitignore

@@ -1 +1,4 @@
-/data/
+/data/
+comments
+test.py
+results.txt

grapher.py

@@ -0,0 +1,69 @@
+import unlearn as unlearner
+from itertools import product
+import json
+#unlearner.unlearn()
+unlearner.unlearn(3,0,8e-3)
+
+exit(0)
+
+
+def generate_grad_map_permutations(base_grad_map=[8, 32, 2000], steps=[2, 8, 200], variations=3):
+    # Generate ranges for each value based on step and variations
+    ranges = [
+        [base + i * step for i in range(-variations, variations + 1)]
+        for base, step in zip(base_grad_map, steps)
+    ]
+    # Generate all permutations using product
+    all_permutations = list(product(*ranges))
+    return all_permutations
+
+# Generate all permutations of target values
+#grad_map_permutations=generate_grad_map_permutations()
+#grad_maps= [[8,32,2000],[8,32,4000],[8,32,1000],[8,16,2000],[8,16,4000],[8,16,1000],[12,32,2000]]
+grad_maps=[[8,32,2000]]
+#all_targets = [(x, y) for x, y in product(range(10), range(10)) if x != y]
+all_targets = [x for x in range(10) if x !=3]
+learning_rates= [4e-3,8e-3,1e-2,2e-2,4e-2]
+batch_sizes = [16]
+epochs_forgets = [4]
+epochs_relearns = [10]
+
+
+# Result storage
+results = []
+
+# Iterate over all combinations
+for target, lr, batch_size, epochs_forget, epochs_relearn,grad_map in product( all_targets, learning_rates, batch_sizes, epochs_forgets, epochs_relearns, grad_maps):
+
+    #forget_target, sub_target = target
+    sub_target=3
+    # Call the unlearn function
+    dataset_score,forgotten_score,new_data_score,static_score,starting_accuracy_forgotten = unlearner.unlearn(target,sub_target,lr,batch_size,epochs_forget,epochs_relearn,grad_map)
+
+    # Store the parameters and outputs together
+    results.append({
+        "parameters": {
+            "forget_target": target,
+            "sub_target": sub_target,
+            "learning_rate": lr,
+            "batch_size": batch_size,
+            "epochs_forget": epochs_forget,
+            "epochs_relearn": epochs_relearn,
+            "grad_map2": grad_map
+        },
+        "results": {
+            "dataset_score": dataset_score,
+            "forgotten_score": forgotten_score,
+            "new_data_score": new_data_score,
+            "static_score": static_score,
+            "starting_accuracy_forgotten": starting_accuracy_forgotten
+        }
+    })
+
+
+# Save to a JSON file
+with open("ablation_results.json", "w") as f:
+    json.dump(results, f, indent=4)
+
+print("Ablation study completed and saved to 'ablation_results.json'")
+print(results)

modelNo0.pth

@@ -0,0 +1,64 @@
+
+forget, sub, lr,        dat_acc,    for_acc,   sub_acc,     static_acc
+0,3,2e-2                64.2%,      0%,         93%,            60.6%
+0,3,1e-2                83.4%,      0%,         45%,            88.3%
+0,3,1.5e-2              79%,        0%,         87.3%,          78.0%
+0,3,1.4e-2              81.8%,      0%,         85.8%,          81.3%
+0,3,1.3e-2              83.7%,      0%,         81.7%,          82.9%
+
+
+1,3,1e-2                77.1%,      0%,         65.9%,          78.5%
+1,3,1.5e-2              75.9%,      0%,         92.8%,          73.7%
+1,3,1.4e-2              76.5%,      0%,         90.9%,          74.7%
+1,3,1.2e-2              77.5%,      0%,         84.0%,          76.7%
+1,3,1.1e-2              77.5%,      0%,         76.9%,          77.6%
+
+
+
+2,3,1.5e-2              58.1%,      0%,         97.0%,          53.1%
+2,3,1.3e-2              62.3%,      0%,         95.1%,          58.2%
+2,3,1e-2                68.8%,      0%,         89.2%,          66.2%
+2,3,8.5e-3              71.1%,      0%,         78.1%,          70.2%
+2,3,8e-3                70.8%,      0%,         68.5%,          71.1%
+
+
+4,3,1e-2                80.5%,      0%,         68.3%,          82.2%
+4,3,1.1e-2              79.8%,      0%,         78.7%,          80%
+4,3,1.3e-2              76.9%,      0%,         89.0%,          75%
+4,3,1.5e-2              73.8%,      0%,         92.0%,          71.4%
+
+5,3,1.1e-2              73.9%,      0%,         36.9%,          78.6%
+5,3,1.3e-2              75.5%,      0%,         69.1%,          76.3%
+5,3,1.4e-2              75.0%,      0%,         80.0%,          74.0%
+
+
+6,3,1.4e-2              84.9%,      0%,         89.9%,          84.3%
+
+7,3,1.3e-2              76.7%,      0%,         95.0%,          74.4%
+7,3,1.2e-2              78.1%,      0%,         93.7%,          76.1%
+7,3,1e-2                81.2%,      0%,         85.2%,          80.7%
+7,3,9e-3                81.7%,      0%,         75.1%,          82.5%
+
+
+8,3,1e-2                77.4%,      0%,         91.8%,          75.6%
+8,3,9e-3                78.4%,      0%,         87.7%,          77.2%
+8,3,8e-3                78.9%,      0%,         80.1%,          78.8%
+
+
+9,3,1e-2                62.2%,      0%,         94.5%,          58.1%
+9,3,8e-3                65.8%,      0%,         84.8%,          63.4%
+9,3,7e-3                67.8%,      0%,         76.7%,          66.7%
+9,3,6.5e-3              68.2%,      0%,         68.1%,          68.2%
+9,3,6e-3                68.9%,      0%,         56.3%,          70.5%
+
+
+
+--------
+
+6,9,4e-3                76.0%,      0%,         89.2%,          74.4%
+6,9,3e-3                78.5%,      0%,         81.4%,          78.2%  
+6,9,2.5e-3              78.6%,      0%,         69.8%,          79.7%   
+
+
+3,9 4e-3                50.2%,      0%,         41.8%,          51.2%
+3,9,4.2e-3              50.6%,      0%,         52.6%,          50.4%

modelNo3.pth

@@ -24,13 +24,15 @@
     transform=ToTensor()
 )
 
+target = 0
+
+# Filter the dataset to exclude the target
+train_mask = training_data.targets != target 
 
-# Filter the dataset to only include classes 0-8
-train_mask = training_data.targets <= 8
 training_data.data = training_data.data[train_mask]
 training_data.targets = training_data.targets[train_mask]
 
-test_mask = test_data.targets <= 8
+test_mask = test_data.targets != target
 test_data.data = test_data.data[test_mask]
 test_data.targets = test_data.targets[test_mask]
 print(training_data.classes)
@@ -118,6 +120,6 @@ def test(dataloader, model):
 print("Done!")
 
 #save the model
-#torch.save(model.state_dict(), "modelNo9.pth")
+torch.save(model.state_dict(), "modelNo"+str(target)+".pth")