Make SequenceLearner points hashable by passing the sequence to the function.

adaptive/learner/sequence_learner.py

@@ -5,8 +5,8 @@
 from adaptive.learner.base_learner import BaseLearner
 
 
-class _IgnoreFirstArgument:
-    """Remove the first argument from the call signature.
+class _IndexToPoint:
+    """Call function with index of sequence.
 
     The SequenceLearner's function receives a tuple ``(index, point)``
     but the original function only takes ``point``.
@@ -15,18 +15,19 @@ class _IgnoreFirstArgument:
     pickable.
     """
 
-    def __init__(self, function):
+    def __init__(self, function, sequence):
         self.function = function
+        self.sequence = sequence
 
-    def __call__(self, index_point, *args, **kwargs):
-        index, point = index_point
+    def __call__(self, index, *args, **kwargs):
+        point = self.sequence[index]
         return self.function(point, *args, **kwargs)
 
     def __getstate__(self):
-        return self.function
+        return self.function, self.sequence
 
-    def __setstate__(self, function):
-        self.__init__(function)
+    def __setstate__(self, state):
+        self.__init__(*state)
 
 
 class SequenceLearner(BaseLearner):
@@ -40,7 +41,7 @@ class SequenceLearner(BaseLearner):
     Parameters
     ----------
     function : callable
-        The function to learn. Must take a single element `sequence`.
+        The function to learn. Must take a single element of `sequence`.
     sequence : sequence
         The sequence to learn.
 
@@ -58,7 +59,7 @@ class SequenceLearner(BaseLearner):
 
     def __init__(self, function, sequence):
         self._original_function = function
-        self.function = _IgnoreFirstArgument(function)
+        self.function = _IndexToPoint(function, sequence)
         self._to_do_indices = SortedSet({i for i, _ in enumerate(sequence)})
         self._ntotal = len(sequence)
         self.sequence = copy(sequence)
@@ -67,31 +68,26 @@ def __init__(self, function, sequence):
 
     def ask(self, n, tell_pending=True):
         indices = []
-        points = []
         loss_improvements = []
         for index in self._to_do_indices:
-            if len(points) >= n:
+            if len(indices) >= n:
                 break
-            point = self.sequence[index]
             indices.append(index)
-            points.append((index, point))
             loss_improvements.append(1 / self._ntotal)
 
         if tell_pending:
-            for i, p in zip(indices, points):
-                self.tell_pending((i, p))
+            for index in indices:
+                self.tell_pending(index)
 
-        return points, loss_improvements
+        return indices, loss_improvements
 
     def _get_data(self):
         return self.data
 
     def _set_data(self, data):
         if data:
             indices, values = zip(*data.items())
-            # the points aren't used by tell, so we can safely pass None
-            points = [(i, None) for i in indices]
-            self.tell_many(points, values)
+            self.tell_many(indices, values)
 
     def loss(self, real=True):
         if not (self._to_do_indices or self.pending_points):
@@ -105,14 +101,12 @@ def remove_unfinished(self):
             self._to_do_indices.add(i)
         self.pending_points = set()
 
-    def tell(self, point, value):
-        index, point = point
+    def tell(self, index, value):
         self.data[index] = value
         self.pending_points.discard(index)
         self._to_do_indices.discard(index)
 
-    def tell_pending(self, point):
-        index, point = point
+    def tell_pending(self, index):
         self.pending_points.add(index)
         self._to_do_indices.discard(index)
 

adaptive/tests/test_learners.py

@@ -281,19 +281,9 @@ def test_adding_existing_data_is_idempotent(learner_type, f, learner_kwargs):
     M = random.randint(10, 30)
     pls = zip(*learner.ask(M))
     cpls = zip(*control.ask(M))
-    if learner_type is SequenceLearner:
-        # The SequenceLearner's points might not be hasable
-        points, values = zip(*pls)
-        indices, points = zip(*points)
 
-        cpoints, cvalues = zip(*cpls)
-        cindices, cpoints = zip(*cpoints)
-        assert (np.array(points) == np.array(cpoints)).all()
-        assert values == cvalues
-        assert indices == cindices
-    else:
-        # Point ordering is not defined, so compare as sets
-        assert set(pls) == set(cpls)
+    # Point ordering is not defined, so compare as sets
+    assert set(pls) == set(cpls)
 
 
 # XXX: This *should* pass (https://github.com/python-adaptive/adaptive/issues/55)
@@ -324,20 +314,9 @@ def test_adding_non_chosen_data(learner_type, f, learner_kwargs):
     pls = zip(*learner.ask(M))
     cpls = zip(*control.ask(M))
 
-    if learner_type is SequenceLearner:
-        # The SequenceLearner's points might not be hasable
-        points, values = zip(*pls)
-        indices, points = zip(*points)
-
-        cpoints, cvalues = zip(*cpls)
-        cindices, cpoints = zip(*cpoints)
-        assert (np.array(points) == np.array(cpoints)).all()
-        assert values == cvalues
-        assert indices == cindices
-    else:
-        # Point ordering within a single call to 'ask'
-        # is not guaranteed to be the same by the API.
-        assert set(pls) == set(cpls)
+    # Point ordering within a single call to 'ask'
+    # is not guaranteed to be the same by the API.
+    assert set(pls) == set(cpls)
 
 
 @run_with(Learner1D, xfail(Learner2D), xfail(LearnerND), AverageLearner)

adaptive/tests/test_sequence_learner.py

@@ -0,0 +1,24 @@
+import asyncio
+
+import numpy as np
+
+import adaptive
+
+
+def might_fail(dct):
+    import random
+
+    if random.random() < 0.5:
+        raise Exception()
+    return dct["x"]
+
+
+def test_fail_with_sequence_of_unhashable():
+    # https://github.com/python-adaptive/adaptive/issues/265
+    seq = [dict(x=x) for x in np.linspace(-1, 1, 101)]  # unhashable
+    learner = adaptive.SequenceLearner(might_fail, sequence=seq)
+    runner = adaptive.Runner(
+        learner, goal=adaptive.SequenceLearner.done, retries=100
+    )  # with 100 retries the test will fail once in 10^31
+    asyncio.get_event_loop().run_until_complete(runner.task)
+    assert runner.status() == "finished"