unlearning CR

unlearning/CertifiedRemoval.py

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+import torch
+import numpy as np
+from scipy.optimize import minimize
+from .Strategy import Strategy
+import torch.nn as nn
+
+class CertifiedRemoval(Strategy):
+    """Implements Certified Removal for machine unlearning."""
+
+    def __init__(self, model, data, labels, removal_bound, epsilon):
+        super().__init__()
+        self.model = model
+        self.data = data
+        self.labels = labels
+        self.removal_bound = removal_bound
+        self.epsilon = epsilon
+
+    def _run(self, model: nn.Module) -> nn.Module:
+        """Runs the certified removal algorithm."""
+        # 1. Linear Model Creation
+        # This is a simplification for demonstration purposes.  In a real implementation,
+        # you'd use more sophisticated methods to learn the parameters of the
+        # 'removal' model based on the example being removed.
+
+        def linear_model(x):
+            return torch.dot(x, torch.tensor([1, 1])) # Simplified Linear Model
+
+        # 2. Optimization for Parameter Adjustment
+        # Optimize the parameter values to minimize the loss while staying within bounds.
+        original_params = torch.tensor([0.0, 0.0])  # Initial parameters for linear model
+
+        def objective_function(params):
+            new_model = linear_model #use same function as defined above
+            return torch.sum(((new_model(self.data[0]) - self.labels)**2))
+                
+        result = minimize(objective_function, original_params, method='L-BFGS-B', bounds=[(-self.removal_bound, self.removal_bound)], options={'maxiter': 100})
+
+        if not result.success:
+            print("Warning: Optimization failed!")
+            print(result.message)
+            return model #Return original if optimization fails
+
+        new_params = result.x
+        # 3. New Model Creation
+
+        new_model = lambda x: torch.dot(x, new_params)
+        return new_model
+
+
+if __name__ == '__main__':
+    # Example Usage - Synthetic Data for Demonstration
+    np.random.seed(42)  # For reproducibility
+    n_samples = 100
+    X = np.random.randn(n_samples, 2)
+    y = (X[:, 0] + X[:, 1] > 0).astype(int)
+
+    # Create a simple linear model for demonstration
+    model = nn.Linear(2, 1)  # Simple linear classifier - PyTorch Version
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # Optimizer for training the linear model
+
+    # Train a Linear Model
+    for _ in range(100): #training loop
+        optimizer.zero_grad()
+        predictions = model(X)
+        loss = torch.sum((predictions - y)**2)
+        loss.backward()
+        optimizer.step()
+
+    # Define parameters for Certified Removal
+    removal_bound = 1.0
+    epsilon = 0.1
+
+    # Create the CertifiedRemoval object with the trained model, data and labels
+    certified_removal_obj = CertifiedRemoval(model, X, y, removal_bound, epsilon)
+
+    # Run Certified Removal
+    new_model = certified_removal_obj.apply(model)