def test_set_reward_signal_nan(self) -> None:
"""Test the reward signal if no new state was generated."""
predictor = MCTS()
state = flexmock()
assert predictor._policy == {}
predictor._next_state = flexmock()
predictor.set_reward_signal(state, ("numpy", "2.0.0", "https://pypi.org/simple"), math.nan)
assert predictor._next_state is None
assert predictor._policy == {}
def test_pre_run(self) -> None:
"""Test calling pre-run for the initialization part."""
flexmock(TemporalDifference)
TemporalDifference.should_receive("pre_run").once()
state = flexmock()
predictor = MCTS()
assert predictor._next_state is None
predictor._next_state = state
predictor.pre_run()
assert predictor._next_state is None
def test_run_next_state(self, context: Context) -> None:
"""Test running the predictor when the next state is scheduled."""
state = flexmock()
unresolved_dependency = ("tensorflow", "2.0.0", "https://pypi.org/simple")
state.should_receive("get_random_unresolved_dependency").with_args(prefer_recent=True).and_return(
unresolved_dependency
).once()
predictor = MCTS()
predictor._next_state = state
context.beam.should_receive("get_last").and_return(state).once()
context.iteration = 1000000 # Some big number not to hit the heat-up part.
with predictor.assigned_context(context):
assert predictor.run() == (state, unresolved_dependency)
def test_run_heat_up(self, context: Context, next_state) -> None:
"""Test running the predictor in the "heat-up" phase regardless next state being set."""
state = flexmock()
unresolved_dependency = ("tensorflow", "2.0.0", "https://pypi.org/simple")
predictor = MCTS()
predictor._next_state = None
flexmock(TemporalDifference)
TemporalDifference.should_receive("run").with_args().and_return(state, unresolved_dependency).once()
context.iteration = 1 # Some small number to hit the heat-up part.
with predictor.assigned_context(context):
assert predictor.run() == (state, unresolved_dependency)
def test_run_next_state_no_last(self, context: Context) -> None:
"""Test running the predictor when the next state is not last state added to beam."""
state = flexmock()
unresolved_dependency = ("tensorflow", "2.0.0", "https://pypi.org/simple")
predictor = MCTS()
predictor._next_state = flexmock()
context.beam.should_receive("get_last").and_return(flexmock()).once()
flexmock(TemporalDifference)
TemporalDifference.should_receive("run").with_args().and_return(state, unresolved_dependency).once()
context.iteration = 1000000 # Some big number not to hit the heat-up part.
with predictor.assigned_context(context):
assert predictor.run() == (state, unresolved_dependency)
def test_set_reward_signal_inf(self) -> None:
"""Test the reward signal if a final state was generated."""
predictor = MCTS()
state = flexmock(score=3.1)
state.should_receive("iter_resolved_dependencies").and_return(
[
("numpy", "2.0.0", "https://pypi.org/simple"),
("tensorflow", "2.0.0", "https://thoth-station.ninja/simple"),
]
)
# numpy was already seen, tensorflow was not seen yet
predictor._policy = {
("numpy", "2.0.0", "https://pypi.org/simple"): [2.3, 100],
}
predictor._next_state = flexmock()
predictor.set_reward_signal(state, ("numpy", "2.0.0", "https://pypi.org/simple"), math.inf)
assert predictor._next_state is None
assert predictor._policy == {
("numpy", "2.0.0", "https://pypi.org/simple"): [2.3 + 3.1, 101],
("tensorflow", "2.0.0", "https://thoth-station.ninja/simple"): [3.1, 1],
}