Resolve "(Learner1D) add possibility to use the direct neighbors in the loss"

Closes #119 (closed)

Currently works for one R^1 \to R^1

Still have to make it work for R^1 \to R^N

Also performance is actually quite good. As in: the learner slows down about 1.5 times. Going (on my laptop) from 3 seconds per 1000 points to 4.5 seconds per 1000 points.

Which, I believe, will be more than compensated by the fact that the chosen points are generally better

adaptive/learner/learner1D.py

@@ -15,7 +15,59 @@ from ..notebook_integration import ensure_holoviews
 from ..utils import cache_latest
 
 
-def uniform_loss(interval, scale, function_values):
+def uses_nth_neighbors(n):
+    """Decorator to specify how many neighboring intervals the loss function uses.
+
+    Wraps loss functions to indicate that they expect intervals together
+    with ``n`` nearest neighbors
+
+    The loss function will then receive the data of the N nearest neighbors 
+    (``nth_neighbors``) aling with the data of the interval itself in a dict.
+    The `~adaptive.Learner1D` will also make sure that the loss is updated
+    whenever one of the ``nth_neighbors`` changes.
+
+    Examples
+    --------
+
+    The next function is a part of the `get_curvature_loss` function.
+
+    >>> @uses_nth_neighbors(1)
+    ...def triangle_loss(xs, ys):
+    ...    xs = [x for x in xs if x is not None]
+    ...    ys = [y for y in ys if y is not None]
+    ...
+    ...    if len(xs) == 2: # we do not have enough points for a triangle
+    ...        return xs[1] - xs[0]
+    ...
+    ...    N = len(xs) - 2 # number of constructed triangles
+    ...    if isinstance(ys[0], Iterable):
+    ...        pts = [(x, *y) for x, y in zip(xs, ys)]
+    ...        vol = simplex_volume_in_embedding
+    ...    else:
+    ...        pts = [(x, y) for x, y in zip(xs, ys)]
+    ...        vol = volume
+    ...    return sum(vol(pts[i:i+3]) for i in range(N)) / N
+
+    Or you may define a loss that favours the (local) minima of a function, 
+    assuming that you know your function will have a single float as output. 
+
+    >>> @uses_nth_neighbors(1)
+    ... def local_minima_resolving_loss(xs, ys):
+    ...     dx = xs[2] - xs[1] # the width of the interval of interest
+    ...
+    ...     if not ((ys[0] is not None and ys[0] > ys[1])
+    ...         or (ys[3] is not None and ys[3] > ys[2])):
+    ...         return loss * 100
+    ...
+    ...     return loss
+    """
+    def _wrapped(loss_per_interval):
+        loss_per_interval.nth_neighbors = n
+        return loss_per_interval
+    return _wrapped
+
+@uses_nth_neighbors(0)
+def uniform_loss(xs, ys):
     """Loss function that samples the domain uniformly.
 
     Works with `~adaptive.Learner1D` only.
@@ -30,37 +82,36 @@ def uniform_loss(interval, scale, function_values):
     ...                              loss_per_interval=uniform_sampling_1d)
     >>>
     """
-    x_left, x_right = interval
-    x_scale, _ = scale
-    dx = (x_right - x_left) / x_scale
+    dx = xs[1] - xs[0]
     return dx
 
 
-def default_loss(interval, scale, function_values):
+@uses_nth_neighbors(0)
+def default_loss(xs, ys):
     """Calculate loss on a single interval.
 
     Currently returns the rescaled length of the interval. If one of the
     y-values is missing, returns 0 (so the intervals with missing data are
     never touched. This behavior should be improved later.
     """
-    x_left, x_right = interval
-    y_right, y_left = function_values[x_right], function_values[x_left]
-    x_scale, y_scale = scale
-    dx = (x_right - x_left) / x_scale
-    if y_scale == 0:
-        loss = dx
+    dx = xs[1] - xs[0]
+    if isinstance(ys[0], Iterable):
+        dy = [abs(a-b) for a, b in zip(*ys)]
+        return np.hypot(dx, dy).max()
     else:
-        dy = (y_right - y_left) / y_scale
-        try:
-            len(dy)
-            loss = np.hypot(dx, dy).max()
-        except TypeError:
-            loss = math.hypot(dx, dy)
-    return loss
+        dy = ys[1] - ys[0]
+        return np.hypot(dx, dy)
 
 
-def _loss_of_multi_interval(xs, ys):
-    N = len(xs) - 2
+@uses_nth_neighbors(1)
+def triangle_loss(xs, ys):
+    xs = [x for x in xs if x is not None]
+    ys = [y for y in ys if y is not None]
+
+    if len(xs) == 2: # we do not have enough points for a triangle
+        return xs[1] - xs[0]
+
+    N = len(xs) - 2 # number of constructed triangles
     if isinstance(ys[0], Iterable):
         pts = [(x, *y) for x, y in zip(xs, ys)]
         vol = simplex_volume_in_embedding
@@ -70,28 +121,15 @@ def _loss_of_multi_interval(xs, ys):
     return sum(vol(pts[i:i+3]) for i in range(N)) / N
 
 
-def triangle_loss(interval, neighbours, scale, function_values):
-    x_left, x_right = interval
-    neighbour_left, neighbour_right = neighbours
-    xs = [neighbour_left, x_left, x_right, neighbour_right]
-    # The neighbours could be None if we are at the boundary, in that case we
-    # have to filter this out
-    xs = [x for x in xs if x is not None]
-
-    if len(xs) <= 2:
-        return (x_right - x_left) / scale[0]
-    else:
-        y_scale = scale[1] or 1
-        ys_scaled = [function_values[x] / y_scale for x in xs]
-        xs_scaled = [x / scale[0] for x in xs]
-        return _loss_of_multi_interval(xs_scaled, ys_scaled)
-
-
 def get_curvature_loss(area_factor=1, euclid_factor=0.02, horizontal_factor=0.02):
-    def curvature_loss(interval, neighbours, scale, function_values):
-        triangle_loss_ = triangle_loss(interval, neighbours, scale, function_values)
-        default_loss_ = default_loss(interval, scale, function_values)
-        dx = (interval[1] - interval[0]) / scale[0]
+    @uses_nth_neighbors(1)
+    def curvature_loss(xs, ys):
+        xs_middle = xs[1:3]
+        ys_middle = xs[1:3]
+
+        triangle_loss_ = triangle_loss(xs, ys)
+        default_loss_ = default_loss(xs_middle, ys_middle)
+        dx = xs_middle[0] - xs_middle[0]
         return (area_factor * (triangle_loss_**0.5)
                 + euclid_factor * default_loss_
                 + horizontal_factor * dx)
@@ -121,6 +159,15 @@ def _get_neighbors_from_list(xs):
     return sortedcontainers.SortedDict(neighbors)
 
 
+def _get_intervals(x, neighbors, nth_neighbors):
+    nn = nth_neighbors
+    i = neighbors.index(x)
+    start = max(0, i - nn - 1)
+    end = min(len(neighbors), i + nn + 2)
+    points = neighbors.keys()[start:end]
+    return list(zip(points, points[1:]))
+
+
 class Learner1D(BaseLearner):
     """Learns and predicts a function 'f:ℝ → ℝ^N'.
 
@@ -145,27 +192,35 @@ class Learner1D(BaseLearner):
 
     Notes
     -----
-    `loss_per_interval` takes 3 parameters: ``interval``,  ``scale``, and
-    ``function_values``, and returns a scalar; the loss over the interval.
-
-    interval : (float, float)
-        The bounds of the interval.
-    scale : (float, float)
-        The x and y scale over all the intervals, useful for rescaling the
-        interval loss.
-    function_values : dict(float → float)
-        A map containing evaluated function values. It is guaranteed
-        to have values for both of the points in 'interval'.
+    `loss_per_interval` takes 2 parameters: ``xs`` and ``ys``, and returns a 
+        scalar; the loss over the interval.
+    xs : tuple of floats
+        The x values of the interval, if `nth_neighbors` is greater than zero it 
+        also contains the x-values of the neighbors of the interval, in ascending 
+        order. The interval we want to know the loss of is then the middle 
+        interval. If no neighbor is available (at the edges of the domain) then 
+        `None` will take the place of the x-value of the neighbor.
+    ys : tuple of function values 
+        The output values of the function when evaluated at the `xs`. This is 
+        either a float or a tuple of floats in the case of vector output.
+
+
+    The `loss_per_interval` function may also have an attribute `nth_neighbors` 
+    that indicates how many of the neighboring intervals to `interval` are used. 
+    If `loss_per_interval` doesn't  have such an attribute, it's assumed that is 
+    uses **no** neighboring intervals. Also see the `uses_nth_neighbors` 
+    decorator for more information.
     """
 
-    def __init__(self, function, bounds, loss_per_interval=None, loss_depends_on_neighbours=False):
+    def __init__(self, function, bounds, loss_per_interval=None):
         self.function = function
-        self._loss_depends_on_neighbours = loss_depends_on_neighbours
 
-        if loss_depends_on_neighbours:
-            self.loss_per_interval = loss_per_interval or get_curvature_loss()
+        if hasattr(loss_per_interval, 'nth_neighbors'):
+            self.nth_neighbors = loss_per_interval.nth_neighbors
         else:
-            self.loss_per_interval = loss_per_interval or default_loss
+            self.nth_neighbors = 0
+
+        self.loss_per_interval = loss_per_interval or default_loss
 
         # A dict storing the loss function for each interval x_n.
         self.losses = {}
@@ -223,24 +278,41 @@ class Learner1D(BaseLearner):
         losses = self.losses if real else self.losses_combined
         return max(losses.values()) if len(losses) > 0 else float('inf')
 
+    def _scale_x(self, x):
+        if x is None: 
+            return None
+        return x / self._scale[0]
+    
+    def _scale_y(self, y):
+        if y is None: 
+            return None
+        y_scale = self._scale[1] or 1
+        return y / y_scale
+
+    def _get_point_by_index(self, ind):
+        if ind < 0 or ind >= len(self.neighbors):
+            return None
+        return self.neighbors.keys()[ind]
+
     def _get_loss_in_interval(self, x_left, x_right):
         assert x_left is not None and x_right is not None
 
-
         if x_right - x_left < self._dx_eps:
             return 0
+            
+        nn = self.nth_neighbors
+        i = self.neighbors.index(x_left)
+        start = i - nn
+        end = i + nn + 2
 
-        # we need to compute the loss for this interval
-        interval = (x_left, x_right)
-        if self._loss_depends_on_neighbours:
-            neighbour_left = self.neighbors.get(x_left, (None, None))[0]
-            neighbour_right = self.neighbors.get(x_right, (None, None))[1]
-            neighbours = neighbour_left, neighbour_right
-            return self.loss_per_interval(interval, neighbours,
-                                          self._scale, self.data)
-        else:
-            return self.loss_per_interval(interval, self._scale, self.data)
+        xs = [self._get_point_by_index(i) for i in range(start, end)]
+        ys = [self.data.get(x, None) for x in xs]
 
+        xs_scaled = tuple(self._scale_x(x) for x in xs)
+        ys_scaled = tuple(self._scale_y(y) for y in ys)
+
+        # we need to compute the loss for this interval
+        return self.loss_per_interval(xs_scaled, ys_scaled)
 
     def _update_interpolated_loss_in_interval(self, x_left, x_right):
         if x_left is None or x_right is None:
@@ -272,17 +344,11 @@ class Learner1D(BaseLearner):
 
         if real:
             # We need to update all interpolated losses in the interval
-            # (x_left, x) and (x, x_right). Since the addition of the point
-            # 'x' could change their loss.
-            self._update_interpolated_loss_in_interval(x_left, x)
-            self._update_interpolated_loss_in_interval(x, x_right)
-
-            # if the loss depends on the neighbors we should also update those losses
-            if self._loss_depends_on_neighbours:
-                neighbour_left = self.neighbors.get(x_left, (None, None))[0]
-                neighbour_right = self.neighbors.get(x_right, (None, None))[1]
-                self._update_interpolated_loss_in_interval(neighbour_left, x_left)
-                self._update_interpolated_loss_in_interval(x_right, neighbour_right)
+            # (x_left, x), (x, x_right) and the nth_neighbors nearest
+            # neighboring intervals. Since the addition of the
+            # point 'x' could change their loss.
+            for ival in _get_intervals(x, self.neighbors, self.nth_neighbors):
+                self._update_interpolated_loss_in_interval(*ival)
 
             # Since 'x' is in between (x_left, x_right),
             # we get rid of the interval.
@@ -291,6 +357,7 @@ class Learner1D(BaseLearner):
         elif x_left is not None and x_right is not None:
             # 'x' happens to be in between two real points,
             # so we can interpolate the losses.
+            print(x_left, x_right)
             dx = x_right - x_left
             loss = self.losses[x_left, x_right]
             self.losses_combined[a, x] = (x - a) * loss / dx
@@ -356,6 +423,9 @@ class Learner1D(BaseLearner):
         if x in self.data:
             # The point is already evaluated before
             return
+        if y is None:
+            raise TypeError("Y-value may not be None, use learner.tell_pending(x)"
+                "to indicate that this value is currently being calculated")
 
         # either it is a float/int, if not, try casting to a np.array
         if not isinstance(y, (float, int)):
@@ -428,10 +498,8 @@ class Learner1D(BaseLearner):
 
         # The the losses for the "real" intervals.
         self.losses = {}
-        for x_left, x_right in intervals:
-            self.losses[x_left, x_right] = (
-                self.loss_per_interval((x_left, x_right), self._scale, self.data)
-                if x_right - x_left >= self._dx_eps else 0)
+        for ival in intervals:
+            self.losses[ival] = self._get_loss_in_interval(*ival)
 
         # List with "real" intervals that have interpolated intervals inside
         to_interpolate = []