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fix problem when learner has no more points for BalancingLearner #214

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fix problem when learner has no more points for BalancingLearner #214

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884674a
fix problem when learner has no more points for BalancingLearner, clo…
basnijholt Sep 4, 2019
2fedb39
fix for strategy="loss" and "npoints"
basnijholt Sep 4, 2019
091d817
add a failing test
basnijholt Sep 4, 2019
8a798f7
make test_asking_more_points_than_available pass
basnijholt Sep 4, 2019
94645b3
rename _to_select -> _ask_all_learners
basnijholt Sep 4, 2019
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32 changes: 21 additions & 11 deletions adaptive/learner/balancing_learner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,20 +119,27 @@ def strategy(self, strategy):
' strategy="npoints", or strategy="cycle" is implemented.'
)

def _to_select(self, total_points):
to_select = []
for index, learner in enumerate(self.learners):
# Take the points from the cache
if index not in self._ask_cache:
self._ask_cache[index] = learner.ask(n=1, tell_pending=False)
points, loss_improvements = self._ask_cache[index]
if not points: # cannot ask for more points
return to_select
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There are a couple of things I don't understand here:

  • seems this return should be a continue; just because learner i could not give any more points does not mean that no other learners can give any!
  • I cannot see when this branch will ever be executed. learner.ask(1) is guaranteed to return a point. At the moment there is no way for a learner to indicate that it has "no more points". If a learner returns no points then it is in violation of the API and other stuff is liable to break

to_select.append(
((index, points[0]), (loss_improvements[0], -total_points[index]))
)
return to_select

def _ask_and_tell_based_on_loss_improvements(self, n):
selected = [] # tuples ((learner_index, point), loss_improvement)
total_points = [l.npoints + len(l.pending_points) for l in self.learners]
for _ in range(n):
to_select = []
for index, learner in enumerate(self.learners):
# Take the points from the cache
if index not in self._ask_cache:
self._ask_cache[index] = learner.ask(n=1, tell_pending=False)
points, loss_improvements = self._ask_cache[index]
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here one would need a double break, but because that doesn't exist I make it into a function.

to_select.append(
((index, points[0]), (loss_improvements[0], -total_points[index]))
)

to_select = self._to_select(total_points)
if not to_select: # cannot ask for more points
break
# Choose the optimal improvement.
(index, point), (loss_improvement, _) = max(to_select, key=itemgetter(1))
total_points[index] += 1
Expand All @@ -156,7 +163,8 @@ def _ask_and_tell_based_on_loss(self, n):
if index not in self._ask_cache:
self._ask_cache[index] = self.learners[index].ask(n=1)
points, loss_improvements = self._ask_cache[index]

if not points: # cannot ask for more points
break
selected.append(((index, points[0]), loss_improvements[0]))
self.tell_pending((index, points[0]))

Expand All @@ -172,6 +180,8 @@ def _ask_and_tell_based_on_npoints(self, n):
if index not in self._ask_cache:
self._ask_cache[index] = self.learners[index].ask(n=1)
points, loss_improvements = self._ask_cache[index]
if not points: # cannot ask for more points
break
total_points[index] += 1
selected.append(((index, points[0]), loss_improvements[0]))
self.tell_pending((index, points[0]))
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