def test_FullMultiplicativeForm_only_minimize():
dm = skcriteria.mkdm(
matrix=[
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
],
objectives=[min, min, min],
)
expected = RankResult(
"FullMultiplicativeForm",
["A0", "A1", "A2"],
[1, 2, 3],
{
"score": np.log([398.42074767, 19.92103738, 4.74310414]),
},
)
transformer = VectorScaler(target="matrix")
dm = transformer.transform(dm)
ranker = FullMultiplicativeForm()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.allclose(result.e_.score, expected.e_.score, atol=1e-4)
def test_FullMultiplicativeForm_only_maximize():
dm = skcriteria.mkdm(
matrix=[
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
],
objectives=[max, max, max],
)
expected = RankResult(
"FullMultiplicativeForm",
["A0", "A1", "A2"],
[3, 2, 1],
{
"score": np.log([0.00682264, 0.13645283, 0.57310187]),
},
)
transformer = VectorScaler(target="matrix")
dm = transformer.transform(dm)
ranker = FullMultiplicativeForm()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.allclose(result.e_.score, expected.e_.score, atol=1e-4)
def test_ReferencePointMOORA_kracka2010ranking():
"""
Data From:
KRACKA, M; BRAUERS, W. K. M.; ZAVADSKAS, E. K. Ranking
Heating Losses in a Building by Applying the MULTIMOORA . -
ISSN 1392 - 2785 Inz
"""
dm = skcriteria.mkdm(
matrix=[
[33.95, 23.78, 11.45, 39.97, 29.44, 167.10, 3.852],
[38.9, 4.17, 6.32, 0.01, 4.29, 132.52, 25.184],
[37.59, 9.36, 8.23, 4.35, 10.22, 136.71, 10.845],
[30.44, 37.59, 13.91, 74.08, 45.10, 198.34, 2.186],
[36.21, 14.79, 9.17, 17.77, 17.06, 148.3, 6.610],
[37.8, 8.55, 7.97, 2.35, 9.25, 134.83, 11.935],
],
objectives=[min, min, min, min, max, min, max],
alternatives=["A1", "A2", "A3", "A4", "A5", "A6"],
criteria=["x1", "x2", "x3", "x4", "x5", "x6", "x7"],
)
expected = RankResult(
"ReferencePointMOORA",
["A1", "A2", "A3", "A4", "A5", "A6"],
[4, 5, 1, 6, 2, 3],
{
"score": [
0.68934931,
0.69986697,
0.59817104,
0.85955696,
0.6002238,
0.61480595,
],
"reference_point": [
0.34587742,
0.08556044,
0.26245184,
0.00011605,
0.77343790,
0.34960423,
0.81382773,
],
},
)
transformer = VectorScaler(target="matrix")
dm = transformer.transform(dm)
ranker = ReferencePointMOORA()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.allclose(result.e_.score, expected.e_.score)
assert np.allclose(result.e_.reference_point, expected.e_.reference_point)
def test_RatioMOORA_kracka2010ranking():
"""
Data From:
KRACKA, M; BRAUERS, W. K. M.; ZAVADSKAS, E. K. Ranking
Heating Losses in a Building by Applying the MULTIMOORA . -
ISSN 1392 - 2785 Inz
"""
dm = skcriteria.mkdm(
matrix=[
[33.95, 23.78, 11.45, 39.97, 29.44, 167.10, 3.852],
[38.9, 4.17, 6.32, 0.01, 4.29, 132.52, 25.184],
[37.59, 9.36, 8.23, 4.35, 10.22, 136.71, 10.845],
[30.44, 37.59, 13.91, 74.08, 45.10, 198.34, 2.186],
[36.21, 14.79, 9.17, 17.77, 17.06, 148.3, 6.610],
[37.8, 8.55, 7.97, 2.35, 9.25, 134.83, 11.935],
],
objectives=[min, min, min, min, max, min, max],
alternatives=["A1", "A2", "A3", "A4", "A5", "A6"],
criteria=["x1", "x2", "x3", "x4", "x5", "x6", "x7"],
)
expected = RankResult(
"RatioMOORA",
["A1", "A2", "A3", "A4", "A5", "A6"],
[5, 1, 3, 6, 4, 2],
{
"score": [
-1.62447867,
-0.25233889,
-0.84635037,
-2.23363519,
-1.18698242,
-0.77456208,
],
},
)
transformer = VectorScaler(target="matrix")
dm = transformer.transform(dm)
ranker = RatioMOORA()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.allclose(result.e_.score, expected.e_.score)
def test_VectorScaler_no_change_original_dm(decision_matrix):
dm = decision_matrix(
seed=42,
min_alternatives=10,
max_alternatives=10,
min_criteria=20,
max_criteria=20,
min_objectives_proportion=0.5,
)
expected = dm.copy()
scaler = VectorScaler(target="both")
dmt = scaler.transform(dm)
assert (
dm.equals(expected) and not dmt.equals(expected) and dm is not expected
)
def test_VectorScaler_simple_weights():
dm = skcriteria.mkdm(
matrix=[[1, 2, 3], [4, 5, 6]],
objectives=[min, max, min],
weights=[1, 2, 3],
)
expected = skcriteria.mkdm(
matrix=[[1, 2, 3], [4, 5, 6]],
objectives=[min, max, min],
weights=[1 / 3.741657387, 2 / 3.741657387, 3 / 3.741657387],
dtypes=[int, int, int],
)
scaler = VectorScaler(target="weights")
result = scaler.transform(dm)
assert result.aequals(expected)
def test_VectorScaler_simple_both():
dm = skcriteria.mkdm(
matrix=[[1, 2, 3], [4, 5, 6]],
objectives=[min, max, min],
weights=[1, 2, 3],
)
expected = skcriteria.mkdm(
matrix=[
[1 / 4.123105626, 2 / 5.385164807, 3 / 6.708203932],
[4 / 4.123105626, 5 / 5.385164807, 6 / 6.708203932],
],
objectives=[min, max, min],
weights=[1 / 3.741657387, 2 / 3.741657387, 3 / 3.741657387],
dtypes=[float, float, float],
)
scaler = VectorScaler(target="both")
result = scaler.transform(dm)
assert result.aequals(expected)
def test_TOPSIS_tzeng2011multiple():
"""
Data from:
Tzeng, G. H., & Huang, J. J. (2011).
Multiple attribute decision making: methods and applications.
CRC press.
"""
dm = skcriteria.mkdm(
matrix=[
[5, 8, 4],
[7, 6, 8],
[8, 8, 6],
[7, 4, 6],
],
objectives=[max, max, max],
weights=[0.3, 0.4, 0.3],
)
transformer = VectorScaler(target="matrix")
dm = transformer.transform(dm)
expected = RankResult(
"TOPSIS",
["A0", "A1", "A2", "A3"],
[3, 2, 1, 4],
{"similarity": [0.5037, 0.6581, 0.7482, 0.3340]},
)
ranker = TOPSIS()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.allclose(
result.e_.similarity, expected.e_.similarity, atol=1.0e-4
)
def test_VectorScaler_both(decision_matrix):
dm = decision_matrix(
seed=42,
min_alternatives=10,
max_alternatives=10,
min_criteria=20,
max_criteria=20,
min_objectives_proportion=0.5,
)
expected = skcriteria.mkdm(
matrix=dm.matrix / np.sqrt(np.sum(np.power(dm.matrix, 2), axis=0)),
objectives=dm.objectives,
weights=dm.weights / np.sqrt(np.sum(np.power(dm.weights, 2))),
alternatives=dm.alternatives,
criteria=dm.criteria,
dtypes=dm.dtypes,
)
scaler = VectorScaler(target="both")
result = scaler.transform(dm)
assert result.aequals(expected)
def test_MultiMOORA_kracka2010ranking():
"""
Data From:
KRACKA, M; BRAUERS, W. K. M.; ZAVADSKAS, E. K. Ranking
Heating Losses in a Building by Applying the MULTIMOORA . -
ISSN 1392 - 2785 Inz
"""
dm = skcriteria.mkdm(
matrix=[
[33.95, 23.78, 11.45, 39.97, 29.44, 167.10, 3.852],
[38.9, 4.17, 6.32, 0.01, 4.29, 132.52, 25.184],
[37.59, 9.36, 8.23, 4.35, 10.22, 136.71, 10.845],
[30.44, 37.59, 13.91, 74.08, 45.10, 198.34, 2.186],
[36.21, 14.79, 9.17, 17.77, 17.06, 148.3, 6.610],
[37.8, 8.55, 7.97, 2.35, 9.25, 134.83, 11.935],
],
objectives=[min, min, min, min, max, min, max],
alternatives=["A1", "A2", "A3", "A4", "A5", "A6"],
criteria=["x1", "x2", "x3", "x4", "x5", "x6", "x7"],
)
expected = RankResult(
"MultiMOORA",
["A1", "A2", "A3", "A4", "A5", "A6"],
[5, 1, 3, 6, 4, 2],
{
"rank_matrix":
np.transpose([
[5, 1, 3, 6, 4, 2],
[4, 5, 1, 6, 2, 3],
[5, 1, 3, 6, 4, 2],
]),
"score": [1, 5, 3, 0, 2, 4],
"ratio_score": [
-1.62447867,
-0.25233889,
-0.84635037,
-2.23363519,
-1.18698242,
-0.77456208,
],
"refpoint_score": [
0.68934931,
0.69986697,
0.59817104,
0.85955696,
0.6002238,
0.61480595,
],
"fmf_score":
np.log([3.4343, 148689.356, 120.3441, 0.7882, 16.2917, 252.9155]),
"reference_point": [
0.34587742,
0.08556044,
0.26245184,
0.00011605,
0.77343790,
0.34960423,
0.81382773,
],
},
)
transformer = VectorScaler(target="matrix")
dm = transformer.transform(dm)
ranker = MultiMOORA()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert np.all(result.e_.rank_matrix == expected.e_.rank_matrix)
assert np.allclose(result.e_.score, expected.e_.score)
assert np.allclose(result.e_.ratio_score, expected.e_.ratio_score)
assert np.allclose(result.e_.refpoint_score, expected.e_.refpoint_score)
assert np.allclose(result.e_.fmf_score, expected.e_.fmf_score, atol=1e-4)
assert np.allclose(result.e_.reference_point, expected.e_.reference_point)