def test_too_few_evals():
X, IncreaseFeature1, IncreaseFeature2, IncreaseFeature3, passed = create_basic_scenario()
possible_actions = [
[IncreaseFeature1(i) for i in range(1,10)],
IncreaseFeature2(5),
[IncreaseFeature3(i) for i in range(1,20)]
]
optimizer = shap.ActionOptimizer(passed, possible_actions)
with pytest.raises(ConvergenceError):
optimizer(X.iloc[0], max_evals=3)
def test_run_out_of_group():
X, IncreaseFeature1, IncreaseFeature2, IncreaseFeature3, passed = create_basic_scenario()
possible_actions = [
[IncreaseFeature1(i) for i in range(1,10)],
IncreaseFeature2(5),
[IncreaseFeature3(1)]
]
optimizer = shap.ActionOptimizer(passed, possible_actions)
actions = optimizer(X.iloc[0])
print(actions)
assert len(actions) == 3
def test_basic_run():
X, IncreaseFeature1, IncreaseFeature2, IncreaseFeature3, passed = create_basic_scenario()
possible_actions = [
[IncreaseFeature1(i) for i in range(1,10)],
IncreaseFeature2(5),
[IncreaseFeature3(i) for i in range(1,20)]
]
optimizer = shap.ActionOptimizer(passed, possible_actions)
actions = optimizer(X.iloc[0])
assert len(actions) == 2
assert sum(a.cost for a in actions) == 27 # ensure we got the optimal answer
def test_bad_action():
with pytest.raises(InvalidAction):
shap.ActionOptimizer(None, [None])