def test_input_validation(self):
with self.assertRaises(TypeError):
Map(False)
with self.assertRaises(TypeError):
Map(lambda: None)
with self.assertRaises(TypeError):
Map(Runnable)
self.assertIsInstance(Map(Runnable()), Runnable)
def test_branch(self):
class Inc(Runnable):
def next(self, state):
return state.updated(cnt=state.cnt + 1)
states = States(State(cnt=1), State(cnt=2))
branch = Map(Inc()) | ArgMin('cnt')
result = branch.run(states).result()
self.assertEqual(result.cnt, states[0].cnt + 1)
def test_map(self):
class A(Runnable, traits.ProblemDecomposer):
def next(self, state):
return state.updated(subproblem=state.problem)
a = A()
m = Map(a)
r = m.run(States(State(problem=1))).result()
self.assertSetEqual(m.inputs, a.inputs)
self.assertSetEqual(m.outputs, a.outputs)
def test_dimensions_match_on_compose(self):
class A(Runnable, traits.ProblemSampler):
def next(self, state):
pass
# dimensionality check
Map(A()) | ArgMin()
with self.assertRaises(TypeError):
A() | ArgMin()
with self.assertRaises(TypeError):
ArgMin() | Map(A())
with self.assertRaises(TypeError):
ArgMin() | Map(ArgMin())
with self.assertRaises(TypeError):
Loop(ArgMin())
def test_map_lambda(self):
states = States(State(cnt=1), State(cnt=2))
result = Map(Lambda(lambda _, s: s.updated(cnt=s.cnt + 1))).run(
states).result()
self.assertEqual(result[0].cnt, states[0].cnt + 1)
self.assertEqual(result[1].cnt, states[1].cnt + 1)
def test_isolated(self):
class Inc(Runnable):
def next(self, state):
return state.updated(cnt=state.cnt + 1)
states = States(State(cnt=1), State(cnt=2))
result = Map(Inc()).run(states).result()
self.assertEqual(result[0].cnt, states[0].cnt + 1)
self.assertEqual(result[1].cnt, states[1].cnt + 1)
