Add pct close to EFRS

policyengine_uk_data/utils/reweight.py

@@ -33,6 +33,12 @@ def loss(weights):
             raise ValueError("Relative error contains NaNs")
         return rel_error.mean()
 
+    def pct_close(weights, t=0.1):
+        # Return the percentage of metrics that are within t% of the target
+        estimate = weights @ loss_matrix
+        abs_error = torch.abs((estimate - targets_array) / (1 + targets_array))
+        return (abs_error < t).sum() / abs_error.numel()
+
     def dropout_weights(weights, p):
         if p == 0:
             return weights
@@ -53,12 +59,15 @@ def dropout_weights(weights, p):
         optimizer.zero_grad()
         weights_ = dropout_weights(weights, dropout_rate)
         l = loss(torch.exp(weights_))
+        close = pct_close(torch.exp(weights_))
         if start_loss is None:
             start_loss = l.item()
         loss_rel_change = (l.item() - start_loss) / start_loss
         l.backward()
         if i % 100 == 0:
-            print(f"Loss: {l.item()}, Rel change: {loss_rel_change}")
+            print(
+                f"Loss: {l.item()}, Rel change: {loss_rel_change}, Epoch: {i}, Within 10%: {close:.2%}"
+            )
         optimizer.step()
 
     return torch.exp(weights).detach().numpy()