def test_DecisionMatrixDominanceAccessor_dominated_strict_false():
dm = data.mkdm(
matrix=[
[10, 80],
[10, 70],
],
objectives=[max, min],
alternatives=["A0", "A1"],
criteria=["C0", "C1"],
)
dom = dominance.DecisionMatrixDominanceAccessor(dm)
strict_expected = pd.Series([False, False], index=["A0", "A1"])
pd.testing.assert_series_equal(dom.dominated(strict=True), strict_expected)
pd.testing.assert_series_equal(dm.dominance.dominated(strict=True),
strict_expected)
not_strict_expected = pd.Series([True, False], index=["A0", "A1"])
pd.testing.assert_series_equal(dom.dominated(strict=False),
not_strict_expected)
pd.testing.assert_series_equal(dm.dominance.dominated(strict=False),
not_strict_expected)
def test_DecisionMatrixDominanceAccessor_dominance_strict():
dm = data.mkdm(
matrix=[
[10, 80],
[20, 70],
],
objectives=[max, min],
alternatives=["A0", "A1"],
criteria=["C0", "C1"],
)
dom = dominance.DecisionMatrixDominanceAccessor(dm)
expected = pd.DataFrame(
[
[False, False],
[True, False],
],
index=["A0", "A1"],
columns=["A0", "A1"],
)
pd.testing.assert_frame_equal(dom.dominance(strict=True), expected)
pd.testing.assert_frame_equal(dm.dominance.dominance(strict=True),
expected)
def test_DecisionMatrixDominanceAccessor_compare():
dm = data.mkdm(
matrix=[
[10, 70],
[20, 70],
],
objectives=[max, min],
alternatives=["A0", "A1"],
criteria=["C0", "C1"],
)
dom = dominance.DecisionMatrixDominanceAccessor(dm)
expected = pd.DataFrame.from_dict({
("Criteria", "C0"): {
("Alternatives", "A0"): False,
("Alternatives", "A1"): True,
("Equals", ""): False,
},
("Criteria", "C1"): {
("Alternatives", "A0"): False,
("Alternatives", "A1"): False,
("Equals", ""): True,
},
("Performance", ""): {
("Alternatives", "A0"): 0,
("Alternatives", "A1"): 1,
("Equals", ""): 1,
},
})
result = dom.compare("A0", "A1")
pd.testing.assert_frame_equal(result, expected)
def test_DecisionMatrixDominanceAccessor_has_loops_false():
dm = data.mkdm(
matrix=[
[10, 80],
[10, 70],
],
objectives=[max, min],
alternatives=["A0", "A1"],
criteria=["C0", "C1"],
)
dom = dominance.DecisionMatrixDominanceAccessor(dm)
assert not dom.has_loops(strict=True)
assert not dom.has_loops(strict=False)
def test_DecisionMatrixDominanceAccessor_dominators_of_strict_false():
dm = data.mkdm(
matrix=[
[10, 80],
[10, 70],
],
objectives=[max, min],
alternatives=["A0", "A1"],
criteria=["C0", "C1"],
)
dom = dominance.DecisionMatrixDominanceAccessor(dm)
assert np.all(dom.dominators_of("A0", strict=False) == ["A1"])
assert not len(dom.dominators_of("A0", strict=True))
def test_DecisionMatrixDominanceAccessor_call_invalid_kind(decision_matrix):
dm = decision_matrix(
seed=42,
min_alternatives=3,
max_alternatives=3,
min_criteria=3,
max_criteria=3,
)
dom = dominance.DecisionMatrixDominanceAccessor(dm)
with pytest.raises(ValueError):
dom("__call__")
dom.zaraza = None # not callable
with pytest.raises(ValueError):
dom("zaraza")
def test_DecisionMatrixDominanceAccessor_call(decision_matrix, kind):
dm = decision_matrix(
seed=42,
min_alternatives=3,
max_alternatives=3,
min_criteria=3,
max_criteria=3,
)
dom = dominance.DecisionMatrixDominanceAccessor(dm)
method_name = (
f"skcriteria.core.dominance.DecisionMatrixDominanceAccessor.{kind}")
with mock.patch(method_name) as plot_method:
dom(kind=kind)
plot_method.assert_called_once()
def test_DecisionMatrixDominanceAccessor_bt():
dm = data.mkdm(
matrix=[
[10, 40],
[20, 70],
],
objectives=[max, min],
alternatives=["A0", "A1"],
criteria=["C0", "C1"],
)
dom = dominance.DecisionMatrixDominanceAccessor(dm)
expected = pd.DataFrame(
[
[0, 1],
[1, 0],
],
index=["A0", "A1"],
columns=["A0", "A1"],
)
pd.testing.assert_frame_equal(dom.bt(), expected)
pd.testing.assert_frame_equal(dm.dominance.bt(), expected)
