def test_run(
self,
state_factory: Callable[[], State],
state_count: int,
limit: int,
count: int,
iteration: int,
accepted_final_states: int,
) -> None:
"""Test running the annealing."""
beam = Beam()
state = state_factory()
for _ in range(state_count):
cloned_state = state.clone()
cloned_state.iteration = state.iteration + 1
beam.add_state(cloned_state)
predictor = AdaptiveSimulatedAnnealing()
context = flexmock(
accepted_final_states_count=accepted_final_states,
count=count,
iteration=iteration,
limit=limit,
beam=beam,
)
with predictor.assigned_context(context):
next_state, package_tuple = predictor.run()
assert next_state in beam.iter_states()
assert package_tuple is not None
assert package_tuple[0] in next_state.unresolved_dependencies
assert package_tuple in next_state.unresolved_dependencies[
package_tuple[0]].values()
def test_pre_run(self) -> None:
"""Test pre-run initialization."""
context = flexmock(limit=100)
predictor = AdaptiveSimulatedAnnealing()
assert predictor._temperature == 0.0
predictor._temperature_history = [(0.1, False, 0.2, 3),
(0.42, True, 0.66, 47)]
with predictor.assigned_context(context):
predictor.pre_run()
assert predictor._temperature == context.limit, "Predictor's limit not initialized correctly"
assert predictor._temperature_history == [], "Predictor's temperature history no discarded"
def test_temperature_function(
self,
t0: float,
accepted_final_states_count: int,
limit: int,
iteration: int,
count: int,
) -> None:
"""Test the temperature function never drops bellow 0."""
context = flexmock(
accepted_final_states_count=accepted_final_states_count,
limit=limit,
iteration=iteration,
count=count,
beam=flexmock(size=96),
)
predictor = AdaptiveSimulatedAnnealing()
assert (predictor._temperature_function(t0=t0, context=context) >=
0.0), "Temperature dropped bellow 0 or is NaN"
