Merge branch 'cache_loss' into 'master'

Cache loss and display it in the live_info widget

See merge request qt/adaptive!117

Author: basnijholt

adaptive/learner/average_learner.py

@@ -4,8 +4,9 @@
 
 import numpy as np
 
-from ..notebook_integration import ensure_holoviews
 from .base_learner import BaseLearner
+from ..notebook_integration import ensure_holoviews
+from ..utils import cache_latest
 
 
 class AverageLearner(BaseLearner):
@@ -90,6 +91,7 @@ def std(self):
             return np.inf
         return sqrt((self.sum_f_sq - n * self.mean**2) / (n - 1))
 
+    @cache_latest
     def loss(self, real=True, *, n=None):
         if n is None:
             n = self.npoints if real else self.n_requested

adaptive/learner/balancing_learner.py

@@ -6,7 +6,7 @@
 
 from .base_learner import BaseLearner
 from ..notebook_integration import ensure_holoviews
-from ..utils import restore, named_product
+from ..utils import cache_latest, named_product, restore
 
 
 def dispatch(child_functions, arg):
@@ -116,6 +116,7 @@ def losses(self, real=True):
 
         return losses
 
+    @cache_latest
     def loss(self, real=True):
         losses = self.losses(real)
         return max(losses)

adaptive/learner/data_saver.py

@@ -1,5 +1,4 @@
 # -*- coding: utf-8 -*-
-
 from collections import OrderedDict
 import functools
 

adaptive/learner/integrator_learner.py

@@ -15,7 +15,7 @@
                                 ndiv_max, min_sep, eps, xi, V_inv,
                                 Vcond, alpha, gamma)
 from ..notebook_integration import ensure_holoviews
-from ..utils import restore
+from ..utils import cache_latest, restore
 
 
 def _downdate(c, nans, depth):
@@ -514,6 +514,7 @@ def done(self):
                 or (err - err_excess < abs(igral) * self.tol < err_excess)
                 or not self.ivals)
 
+    @cache_latest
     def loss(self, real=True):
         return abs(abs(self.igral) * self.tol - self.err)
 

adaptive/learner/learner1D.py

@@ -7,8 +7,9 @@
 import numpy as np
 import sortedcontainers
 
-from ..notebook_integration import ensure_holoviews
 from .base_learner import BaseLearner
+from ..notebook_integration import ensure_holoviews
+from ..utils import cache_latest
 
 
 def uniform_loss(interval, scale, function_values):
@@ -156,6 +157,7 @@ def vdim(self):
     def npoints(self):
         return len(self.data)
 
+    @cache_latest
     def loss(self, real=True):
         losses = self.losses if real else self.losses_combined
         return max(losses.values()) if len(losses) > 0 else float('inf')

adaptive/learner/learner2D.py

@@ -7,8 +7,9 @@
 import numpy as np
 from scipy import interpolate
 
-from ..notebook_integration import ensure_holoviews
 from .base_learner import BaseLearner
+from ..notebook_integration import ensure_holoviews
+from ..utils import cache_latest
 
 
 # Learner2D and helper functions.
@@ -267,7 +268,6 @@ def __init__(self, function, bounds, loss_per_triangle=None):
         self._stack.update({p: np.inf for p in self._bounds_points})
         self.function = function
         self._ip = self._ip_combined = None
-        self._loss = np.inf
 
         self.stack_size = 10
 
@@ -438,13 +438,13 @@ def ask(self, n, tell_pending=True):
 
         return points[:n], loss_improvements[:n]
 
+    @cache_latest
     def loss(self, real=True):
         if not self.bounds_are_done:
             return np.inf
         ip = self.ip() if real else self.ip_combined()
         losses = self.loss_per_triangle(ip)
-        self._loss = losses.max()
-        return self._loss
+        return losses.max()
 
     def remove_unfinished(self):
         self.pending_points = set()

adaptive/learner/learnerND.py

@@ -13,7 +13,7 @@
 from ..notebook_integration import ensure_holoviews
 from .triangulation import (Triangulation, point_in_simplex,
                             circumsphere, simplex_volume_in_embedding)
-from ..utils import restore
+from ..utils import restore, cache_latest
 
 
 def volume(simplex, ys=None):
@@ -452,6 +452,7 @@ def losses(self):
 
         return self._losses
 
+    @cache_latest
     def loss(self, real=True):
         losses = self.losses()  # XXX: compute pending loss if real == False
         return max(losses.values()) if losses else float('inf')

adaptive/learner/skopt_learner.py

@@ -1,11 +1,10 @@
 # -*- coding: utf-8 -*-
-
 import numpy as np
+from skopt import Optimizer
 
-from ..notebook_integration import ensure_holoviews
 from .base_learner import BaseLearner
-
-from skopt import Optimizer
+from ..notebook_integration import ensure_holoviews
+from ..utils import restore, cache_latest
 
 
 class SKOptLearner(Optimizer, BaseLearner):
@@ -38,6 +37,7 @@ def tell_pending(self, x):
     def remove_unfinished(self):
         pass
 
+    @cache_latest
     def loss(self, real=True):
         if not self.models:
             return np.inf

adaptive/notebook_integration.py

@@ -174,6 +174,9 @@ def _info_html(runner):
     with suppress(Exception):
         info.append(('# of points', runner.learner.npoints))
 
+    with suppress(Exception):
+        info.append(('latest loss', f'{runner.learner._cache["loss"]:.3f}'))
+
     template = '<dt>{}</dt><dd>{}</dd>'
     table = '\n'.join(template.format(k, v) for k, v in info)
 

adaptive/utils.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 from contextlib import contextmanager
+from functools import wraps
 from itertools import product
 import time
 
@@ -24,3 +25,16 @@ def restore(*learners):
     finally:
         for state, learner in zip(states, learners):
             learner.__setstate__(state)
+
+
+def cache_latest(f):
+    """Cache the latest return value of the function and add it
+    as 'self._cache[f.__name__]'."""
+    @wraps(f)
+    def wrapper(*args, **kwargs):
+        self = args[0]
+        if not hasattr(self, '_cache'):
+            self._cache = {}
+        self._cache[f.__name__] = f(*args, **kwargs)
+        return self._cache[f.__name__]
+    return wrapper