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 = TemporalDifference()
assert predictor._temperature_function(
t0=t0,
context=context) >= 0.0, "Temperature dropped bellow 0 or is NaN"
def test_run_exploitation(self, context: Context) -> None:
"""Tests run when exploitation is performed."""
flexmock(TemporalDifference)
flexmock(AdaptiveSimulatedAnnealing)
max_state = State(score=3.0)
probable_state = State(score=2.0)
context.beam.add_state(max_state)
context.beam.add_state(probable_state)
unresolved_dependency = (
"pytorch",
"1.0.0",
"https://thoth-station.ninja/simple",
)
flexmock(random)
random.should_receive("randrange").with_args(1, 2).and_return(0).once()
random.should_receive("random").and_return(0.99).once(
) # *higher* than acceptance_probability that is 0.75 so we do exploitation
TemporalDifference.should_receive("_do_exploitation").with_args(
max_state).and_return(unresolved_dependency).once()
TemporalDifference.should_receive("_temperature_function").with_args(
1.0, context).and_return(0.9).once()
AdaptiveSimulatedAnnealing.should_receive(
"_compute_acceptance_probability").with_args(
max_state.score, probable_state.score,
0.9).and_return(0.75).once()
context.beam.should_receive("max").with_args().and_return(
max_state).once()
predictor = TemporalDifference(step=1)
predictor._temperature = 1.0
predictor._steps_taken = 0
with predictor.assigned_context(context):
assert predictor.run() == (max_state, unresolved_dependency)
assert predictor._steps_taken == 1
def test_pre_run(self) -> None:
"""Test initialization done before running."""
predictor = TemporalDifference()
predictor._policy = {("tensorflow", "2.0.0", "https://pypi.org/simple"): [1.0, 2]}
predictor._temperature_history = [(0.212, True, 0.23, 100)]
predictor._temperature = 12.3
context = flexmock(limit=42)
with predictor.assigned_context(context):
predictor.pre_run()
assert predictor._policy == {}
assert predictor._temperature_history == []
assert isinstance(predictor._temperature, float)
assert predictor._temperature == float(context.limit)
def test_n_step_td_step_adjust(self, context: Context) -> None:
"""Test adjusting steps taken on reward signal propagation."""
predictor = TemporalDifference(step=1)
predictor._temperature = 1.0
predictor._steps_taken = 1
package_tuple = ("tensorflow", "2.3.1", "https://pypi.org/simple")
state = State()
state.add_resolved_dependency(package_tuple)
with predictor.assigned_context(context):
predictor.set_reward_signal(state, package_tuple, 0.33)
assert predictor._policy.get(package_tuple) == [0.33, 1]
assert predictor._steps_taken == 0
def test_do_exploitation(self) -> None:
"""Tests on exploitation computation."""
predictor = TemporalDifference()
predictor._policy = {
("tensorflow", "2.1.0", "https://thoth-station.ninja"):
[2020.21, 666],
("tensorflow", "2.0.0", "https://thoth-station.ninja"):
[16.61, 1992],
("numpy", "1.0.0", "https://pypi.org/simple"): [30.30, 92],
}
state = flexmock()
state.should_receive("iter_unresolved_dependencies").and_return([
("spacy", "2.2.4", "https://pypi.org/simple"),
("numpy", "1.0.0", "https://pypi.org/simple"),
("tensorflow", "2.1.0", "https://thoth-station.ninja"),
]).once()
state.should_receive("get_random_unresolved_dependency").times(0)
assert predictor._do_exploitation(state) == (
"tensorflow",
"2.1.0",
"https://thoth-station.ninja",
)
def test_set_reward_signal_unseen(self) -> None:
"""Test keeping the reward signal for an unseen step."""
reward = 42.24
package_tuple = ("tensorflow", "2.0.0", "https://thoth-station.ninja")
state = flexmock()
state.should_receive("iter_resolved_dependencies").and_return([package_tuple]).once()
predictor = TemporalDifference()
predictor._policy = {
("numpy", "1.0.0", "https://pypi.org/simple"): [30.30, 92],
}
predictor._steps_taken = 1
predictor.set_reward_signal(state, None, reward)
assert predictor._policy == {
package_tuple: [42.24, 1],
("numpy", "1.0.0", "https://pypi.org/simple"): [30.30, 92],
}
def test_init(self) -> None:
"""Test instantiation."""
predictor = TemporalDifference()
assert predictor._policy == {}
assert predictor._temperature_history == []
assert predictor._temperature == 0.0
def test_step_default(self) -> None:
"""Test default parameter for step."""
predictor = TemporalDifference()
assert predictor.step == 1
assert predictor._steps_taken == 0
