Merge pull request #219 from python-adaptive/value_scale_ND

pass value_scale to the LearnerND's loss_per_simplex function

adaptive/learner/learnerND.py

@@ -47,30 +47,78 @@ def orientation(simplex):
     return sign
 
 
-def uniform_loss(simplex, ys=None):
+def uniform_loss(simplex, values, value_scale):
+    """
+    Uniform loss.
+
+    Parameters
+    ----------
+    simplex : list of tuples
+        Each entry is one point of the simplex.
+    values : list of values
+        The scaled function values of each of the simplex points.
+    value_scale : float
+        The scale of values, where ``values = function_values * value_scale``.
+
+    Returns
+    -------
+    loss : float
+    """
     return volume(simplex)
 
 
-def std_loss(simplex, ys):
-    r = np.linalg.norm(np.std(ys, axis=0))
+def std_loss(simplex, values, value_scale):
+    """
+    Computes the loss of the simplex based on the standard deviation.
+
+    Parameters
+    ----------
+    simplex : list of tuples
+        Each entry is one point of the simplex.
+    values : list of values
+        The scaled function values of each of the simplex points.
+    value_scale : float
+        The scale of values, where ``values = function_values * value_scale``.
+
+    Returns
+    -------
+    loss : float
+    """
+
+    r = np.linalg.norm(np.std(values, axis=0))
     vol = volume(simplex)
 
     dim = len(simplex) - 1
 
     return r.flat * np.power(vol, 1.0 / dim) + vol
 
 
-def default_loss(simplex, ys):
-    # return std_loss(simplex, ys)
-    if isinstance(ys[0], Iterable):
-        pts = [(*x, *y) for x, y in zip(simplex, ys)]
+def default_loss(simplex, values, value_scale):
+    """
+    Computes the average of the volumes of the simplex.
+
+    Parameters
+    ----------
+    simplex : list of tuples
+        Each entry is one point of the simplex.
+    values : list of values
+        The scaled function values of each of the simplex points.
+    value_scale : float
+        The scale of values, where ``values = function_values * value_scale``.
+
+    Returns
+    -------
+    loss : float
+    """
+    if isinstance(values[0], Iterable):
+        pts = [(*x, *y) for x, y in zip(simplex, values)]
     else:
-        pts = [(*x, y) for x, y in zip(simplex, ys)]
+        pts = [(*x, y) for x, y in zip(simplex, values)]
     return simplex_volume_in_embedding(pts)
 
 
 @uses_nth_neighbors(1)
-def triangle_loss(simplex, values, neighbors, neighbor_values):
+def triangle_loss(simplex, values, value_scale, neighbors, neighbor_values):
     """
     Computes the average of the volumes of the simplex combined with each
     neighbouring point.
@@ -80,7 +128,9 @@ def triangle_loss(simplex, values, neighbors, neighbor_values):
     simplex : list of tuples
         Each entry is one point of the simplex.
     values : list of values
-        The function values of each of the simplex points.
+        The scaled function values of each of the simplex points.
+    value_scale : float
+        The scale of values, where ``values = function_values * value_scale``.
     neighbors : list of tuples
         The neighboring points of the simplex, ordered such that simplex[0]
         exacly opposes neighbors[0], etc.
@@ -108,20 +158,22 @@ def triangle_loss(simplex, values, neighbors, neighbor_values):
 def curvature_loss_function(exploration=0.05):
     # XXX: add doc-string!
     @uses_nth_neighbors(1)
-    def curvature_loss(simplex, values, neighbors, neighbor_values):
+    def curvature_loss(simplex, values, value_scale, neighbors, neighbor_values):
         """Compute the curvature loss of a simplex.
 
         Parameters
         ----------
         simplex : list of tuples
             Each entry is one point of the simplex.
         values : list of values
-            The function values of each of the simplex points.
+            The scaled function values of each of the simplex points.
+        value_scale : float
+            The scale of values, where ``values = function_values * value_scale``.
         neighbors : list of tuples
             The neighboring points of the simplex, ordered such that simplex[0]
             exacly opposes neighbors[0], etc.
         neighbor_values : list of values
-            The function values for each of the neighboring points.
+            The scaled function values for each of the neighboring points.
 
         Returns
         -------
@@ -130,7 +182,9 @@ def curvature_loss(simplex, values, neighbors, neighbor_values):
         dim = len(simplex[0])  # the number of coordinates
         loss_input_volume = volume(simplex)
 
-        loss_curvature = triangle_loss(simplex, values, neighbors, neighbor_values)
+        loss_curvature = triangle_loss(
+            simplex, values, value_scale, neighbors, neighbor_values
+        )
         return (
             loss_curvature + exploration * loss_input_volume ** ((2 + dim) / dim)
         ) ** (1 / (2 + dim))
@@ -563,7 +617,9 @@ def _compute_loss(self, simplex):
 
         if self.nth_neighbors == 0:
             # compute the loss on the scaled simplex
-            return float(self.loss_per_simplex(vertices, values))
+            return float(
+                self.loss_per_simplex(vertices, values, self._output_multiplier)
+            )
 
         # We do need the neighbors
         neighbors = self.tri.get_opposing_vertices(simplex)
@@ -580,7 +636,13 @@ def _compute_loss(self, simplex):
                 neighbor_values[i] = self._output_multiplier * value
 
         return float(
-            self.loss_per_simplex(vertices, values, neighbor_points, neighbor_values)
+            self.loss_per_simplex(
+                vertices,
+                values,
+                self._output_multiplier,
+                neighbor_points,
+                neighbor_values,
+            )
         )
 
     def _update_losses(self, to_delete: set, to_add: set):