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_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_WeightedProductModel_enwiki_1015567716():
"""
Data from:
Weighted product model. (n.d.). Retrieved January 07, 2017,
from http://en.wikipedia.org/wiki/Weighted_product_model
"""
dm = skcriteria.mkdm(
matrix=[
[25, 20, 15, 30],
[10, 30, 20, 30],
[30, 10, 30, 10],
],
objectives=[max, max, max, max],
weights=[20, 15, 40, 25],
)
expected = RankResult(
"WeightedProductModel",
["A0", "A1", "A2"],
[1, 2, 3],
{"score": [-0.50128589, -0.50448471, -0.52947246]},
)
transformer = SumScaler(target="both")
dm = transformer.transform(dm)
ranker = WeightedProductModel()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.allclose(result.e_.score, expected.e_.score)
def test_TOPSIS():
dm = skcriteria.mkdm(
matrix=[[1, 0, 3], [0, 5, 6]],
objectives=[max, max, max],
)
expected = RankResult(
"TOPSIS",
["A0", "A1"],
[2, 1],
{
"ideal": [1, 5, 6],
"anti_ideal": [0, 0, 3],
"similarity": [0.14639248, 0.85360752],
},
)
ranker = TOPSIS()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.all(result.e_.ideal == expected.e_.ideal)
assert np.allclose(result.e_.anti_ideal, expected.e_.anti_ideal)
assert np.allclose(result.e_.similarity, expected.e_.similarity)
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_RankResult_invalid_rank(rank):
method = "foo"
alternatives = ["a", "b", "c"]
extra = {"alfa": 1}
with pytest.raises(ValueError):
RankResult(method=method,
alternatives=alternatives,
values=rank,
extra=extra)
def test_WeightedSumModel_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 Inzinerine Ekonomika-Engineering Economics, 2010,
21(4), 352-359.
"""
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],
weights=[20, 20, 20, 20, 20, 20, 20],
)
transformers = [
InvertMinimize(),
SumScaler(target="both"),
]
for t in transformers:
dm = t.transform(dm)
expected = RankResult(
"WeightedSumModel",
["A0", "A1", "A2", "A3", "A4", "A5"],
[6, 1, 3, 4, 5, 2],
{
"score": [
0.12040426,
0.3458235,
0.13838192,
0.12841246,
0.12346084,
0.14351701,
]
},
)
ranker = WeightedSumModel()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.allclose(result.e_.score, expected.e_.score)
def test_RankResult_repr():
method = "foo"
alternatives = ["a", "b", "c"]
rank = [1, 2, 3]
extra = {"alfa": 1}
result = RankResult(method=method,
alternatives=alternatives,
values=rank,
extra=extra)
expected = " a b c\n" "Rank 1 2 3\n" "[Method: foo]"
assert repr(result) == expected
def test_RankResult_len():
random = np.random.default_rng(seed=42)
length = random.integers(10, 100)
rank = np.arange(length) + 1
alternatives = [f"A.{r}" for r in rank]
method = "foo"
extra = {}
result = RankResult(method=method,
alternatives=alternatives,
values=rank,
extra=extra)
assert len(result) == length
def test_RankResult(rank, has_ties, untied_rank):
method = "foo"
alternatives = ["a", "b", "c"]
extra = {"alfa": 1}
result = RankResult(method=method,
alternatives=alternatives,
values=rank,
extra=extra)
assert result.has_ties_ == has_ties
assert result.ties_ == Counter(result.rank_)
assert np.all(result.method == method)
assert np.all(result.alternatives == alternatives)
assert np.all(result.rank_ == rank)
assert np.all(result.extra_ == result.e_ == extra)
assert np.all(result.untied_rank_ == untied_rank)
def test_WeightedSumModel():
dm = skcriteria.mkdm(
matrix=[[1, 0, 3], [0, 5, 6]],
objectives=[max, max, max],
)
expected = RankResult("WeightedSumModel", ["A0", "A1"], [2, 1],
{"score": [4.0, 11.0]})
ranker = WeightedSumModel()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.all(result.e_.score == expected.e_.score)
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_SIMUS_munier24metodo():
"""
Data From:
Munier, N., Carignano, C., & Alberto, C.
UN MÉTODO DE PROGRAMACIÓN MULTIOBJETIVO.
Revista de la Escuela de Perfeccionamiento en Investigación
Operativa, 24(39).
"""
dm = skcriteria.mkdm(
matrix=[
[250, 120, 20, 800],
[130, 200, 40, 1000],
[350, 340, 15, 600],
],
objectives=[max, max, min, max],
alternatives=["Proyecto 1", "Proyecto 2", "Proyecto 3"],
criteria=["Criterio 1", "Criterio 2", "Criterio 3", "Criterio 4"],
)
b = [None, 500, None, None]
expected = RankResult(
"SIMUS",
["Proyecto 1", "Proyecto 2", "Proyecto 3"],
[3, 2, 1],
{
"method_1_score": [0.09090909, 0.66603535, 0.74305556],
"method_2_score": [-2.45454545, 0.99621211, 1.45833334],
"rank_by": 1,
},
)
ranker = SIMUS()
result = ranker.evaluate(dm, b=b)
assert result.equals(expected)
assert result.method == expected.method
assert np.all(result.e_.b == b)
assert np.all(result.e_.rank_by == expected.e_.rank_by)
assert np.allclose(result.e_.method_1_score, expected.e_.method_1_score)
assert np.allclose(result.e_.method_2_score, expected.e_.method_2_score)
def test_WeightedProductModel():
dm = skcriteria.mkdm(
matrix=[[1, 2, 3], [4, 5, 6]],
objectives=[max, max, max],
)
expected = RankResult(
"WeightedProductModel",
["A0", "A1"],
[2, 1],
{"score": [0.77815125, 2.07918125]},
)
ranker = WeightedProductModel()
result = ranker.evaluate(dm)
assert result.equals(expected)
assert result.method == expected.method
assert np.allclose(result.e_.score, expected.e_.score)
def test_RankResult_repr_html():
method = "foo"
alternatives = ["a", "b", "c"]
rank = [1, 2, 3]
extra = {"alfa": 1}
result = PyQuery(
RankResult(method=method,
alternatives=alternatives,
values=rank,
extra=extra)._repr_html_())
expected = PyQuery("""
<div class='skcresult skcresult-rank'>
<table id="T_cc7f5_" >
<thead>
<tr>
<th class="blank level0" ></th>
<th class="col_heading level0 col0" >a</th>
<th class="col_heading level0 col1" >b</th>
<th class="col_heading level0 col2" >c</th>
</tr>
</thead>
<tbody>
<tr>
<th id="T_cc7f5_level0_row0" class="row_heading level0 row0" >
Rank
</th>
<td id="T_cc7f5_row0_col0" class="data row0 col0" >1</td>
<td id="T_cc7f5_row0_col1" class="data row0 col1" >2</td>
<td id="T_cc7f5_row0_col2" class="data row0 col2" >3</td>
</tr>
</tbody>
</table>
<em class='rankresult-method'>Method: foo</em>
</div>
""")
assert result.remove("style").text() == expected.remove("style").text()
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_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)
