More detailed defaults for problem.get_full_vector (#1393)

* Made get_full_vector more intuitive

* Corrected he usage of get_full vector. Removed it for lb and ub in favor of lb_full and ub_full.

* Apply suggestions from code review

Co-authored-by: Maren Philipps <55318391+m-philipps@users.noreply.github.com>

---------

Co-authored-by: Maren Philipps <55318391+m-philipps@users.noreply.github.com>

pypesto/problem/base.py

@@ -332,7 +332,10 @@ def unfix_parameters(
         self.normalize()
 
     def get_full_vector(
-        self, x: Union[np.ndarray, None], x_fixed_vals: Iterable[float] = None
+        self,
+        x: Union[np.ndarray, None],
+        x_fixed_vals: Iterable[float] = None,
+        x_is_grad: bool = False,
     ) -> Union[np.ndarray, None]:
         """
         Map vector from dim to dim_full. Usually used for x, grad.
@@ -342,9 +345,9 @@ def get_full_vector(
         x: array_like, shape=(dim,)
             The vector in dimension dim.
         x_fixed_vals: array_like, ndim=1, optional
-            The values to be used for the fixed indices. If None, then nans are
-            inserted. Usually, None will be used for grad and
-            problem.x_fixed_vals for x.
+            The values to be used for the fixed indices. If None and x_is_grad=False, problem.x_fixed_vals is used; for x_is_grad=True, nans are inserted.
+        x_is_grad: bool
+            If true, x is treated as gradients.
         """
         if x is None:
             return None
@@ -362,6 +365,9 @@ def get_full_vector(
         x_full[..., self.x_free_indices] = x
         if x_fixed_vals is not None:
             x_full[..., self.x_fixed_indices] = x_fixed_vals
+            return x_full
+        if not x_is_grad:
+            x_full[..., self.x_fixed_indices] = self.x_fixed_vals
         return x_full
 
     def get_full_matrix(

pypesto/result/optimize.py

@@ -191,10 +191,10 @@ def update_to_full(self, problem: Problem) -> None:
             problem which contains info about how to convert to full vectors
             or matrices
         """
-        self.x = problem.get_full_vector(self.x, problem.x_fixed_vals)
-        self.grad = problem.get_full_vector(self.grad)
+        self.x = problem.get_full_vector(self.x)
+        self.grad = problem.get_full_vector(self.grad, x_is_grad=True)
         self.hess = problem.get_full_matrix(self.hess)
-        self.x0 = problem.get_full_vector(self.x0, problem.x_fixed_vals)
+        self.x0 = problem.get_full_vector(self.x0)
         self.free_indices = np.array(problem.x_free_indices)
 
 

pypesto/visualize/parameters.py

@@ -417,8 +417,8 @@ def handle_inputs(
         ub = result.problem.get_reduced_vector(ub, parameter_indices)
         x_labels = [x_labels[int(i)] for i in parameter_indices]
     else:
-        lb = result.problem.get_full_vector(lb)
-        ub = result.problem.get_full_vector(ub)
+        lb = result.problem.lb_full
+        ub = result.problem.ub_full
 
     if inner_xs is not None and plot_inner_parameters:
         lb = np.concatenate([lb, inner_lb])

test/base/test_history.py

@@ -236,9 +236,7 @@ def check_reconstruct_history(
 
     def check_history_consistency(self, start: pypesto.OptimizerResult):
         def xfull(x_trace):
-            return self.problem.get_full_vector(
-                x_trace, self.problem.x_fixed_vals
-            )
+            return self.problem.get_full_vector(x_trace)
 
         if isinstance(start.history, (CsvHistory, Hdf5History)):
             # get index of optimal parameter

test/base/test_x_fixed.py

@@ -41,8 +41,7 @@ def test_optimize():
     # fixed values written into parameter vector
     assert optimizer_result.x[1] == 1
 
-    lb_full = problem.get_full_vector(problem.lb)
-    assert len(lb_full) == 5
+    assert len(problem.lb_full) == 5
 
 
 def create_problem():