01_one_dim: use new extract_model_params() defaults

BLcourse2.3/01_one_dim.py

@@ -171,7 +171,7 @@ model = ExactGPModel(X_train, y_train, likelihood)
 print(model)
 
 # Default start hyper params
-pprint(extract_model_params(model, raw=False))
+pprint(extract_model_params(model))
 
 # +
 # Set new start hyper params
@@ -180,7 +180,7 @@ model.covar_module.base_kernel.lengthscale = 1.0
 model.covar_module.outputscale = 1.0
 model.likelihood.noise_covar.noise = 1e-3
 
-pprint(extract_model_params(model, raw=False))
+pprint(extract_model_params(model))
 # -
 
 
@@ -347,7 +347,7 @@ for ii in range(n_iter):
     optimizer.step()
     if (ii + 1) % 10 == 0:
         print(f"iter {ii + 1}/{n_iter}, {loss=:.3f}")
-    for p_name, p_val in extract_model_params(model).items():
+    for p_name, p_val in extract_model_params(model, try_item=True).items():
         history[p_name].append(p_val)
     history["loss"].append(loss.item())
 # -
@@ -361,7 +361,7 @@ for ax, (p_name, p_lst) in zip(axs, history.items()):
     ax.set_xlabel("iterations")
 
 # Values of optimized hyper params
-pprint(extract_model_params(model, raw=False))
+pprint(extract_model_params(model))
 
 # We see that all hyper params converge. In particular, note that the constant
 # mean $m(\ve x)=c$ converges to the `const` value in `generate_data()`.
@@ -489,7 +489,7 @@ print(
 print(
     "learned noise:",
     np.sqrt(
-        extract_model_params(model, raw=False)["likelihood.noise_covar.noise"]
+        extract_model_params(model, try_item=True)["likelihood.noise_covar.noise"]
     ),
 )
 # -