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_SumScaler_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,
)
matrix = dm.matrix.to_numpy()
expected = skcriteria.mkdm(
matrix=matrix / np.sum(matrix, axis=0, keepdims=True, dtype=float),
objectives=dm.objectives,
weights=dm.weights / np.sum(dm.weights),
alternatives=dm.alternatives,
criteria=dm.criteria,
dtypes=dm.dtypes,
)
scaler = SumScaler(target="both")
result = scaler.transform(dm)
assert result.equals(expected)
def test_ELECTRE2_cebrian2009localizacion():
"""
Data From:
Cebrián, L. I. G., & Porcar, A. M. (2009). Localización empresarial
en Aragón: Una aplicación empírica de la ayuda a la decisión
multicriterio tipo ELECTRE I y III. Robustez de los resultados
obtenidos.
Revista de Métodos Cuantitativos para la Economía y la Empresa,
(7), 31-56.
"""
dm = skcriteria.mkdm(
matrix=[
[6, 5, 28, 5, 5],
[4, 2, 25, 10, 9],
[5, 7, 35, 9, 6],
[6, 1, 27, 6, 7],
[6, 8, 30, 7, 9],
[5, 6, 26, 4, 8],
],
objectives=[1, 1, -1, 1, 1],
weights=[0.25, 0.25, 0.1, 0.2, 0.2],
)
scaler = SumScaler("both")
dm = scaler.transform(dm)
kselector = ELECTRE2()
result = kselector.evaluate(dm)
assert np.all(result.rank_ == [3, 2, 1, 3, 1, 3])
np.testing.assert_allclose(result.e_.score, [2.0, 1.5, 1.0, 2.0, 1.0, 2.0])
def test_SumScaler_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 = SumScaler(target="both")
dmt = scaler.transform(dm)
assert (
dm.equals(expected) and not dmt.equals(expected) and dm is not expected
)
def test_SumScaler_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 / 5, 2 / 7, 3 / 9], [4 / 5, 5 / 7, 6 / 9]],
objectives=[min, max, min],
weights=[1 / 6, 2 / 6, 3 / 6],
dtypes=[float, float, float],
)
scaler = SumScaler(target="both")
result = scaler.transform(dm)
assert result.equals(expected)
def test_SumScaler_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 / 6, 2 / 6, 3 / 6],
dtypes=[int, int, int],
)
scaler = SumScaler(target="weights")
result = scaler.transform(dm)
assert result.equals(expected)
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_ELECTRE1_kernel_sensibility_barba1997decisiones():
"""
Data From:
Barba-Romero, S., & Pomerol, J. C. (1997).
Decisiones multicriterio: fundamentos teóricos y utilización
práctica. P.216
"""
dm = skcriteria.mkdm(
matrix=[
[0.188, 0.172, 0.168, 0.122, 0.114],
[0.125, 0.069, 0.188, 0.244, 0.205],
[0.156, 0.241, 0.134, 0.220, 0.136],
[0.188, 0.034, 0.174, 0.146, 0.159],
[0.188, 0.276, 0.156, 0.171, 0.205],
[0.156, 0.207, 0.180, 0.098, 0.182],
],
weights=[0.25, 0.25, 0.10, 0.20, 0.20],
objectives=[1, 1, 1, 1, 1],
alternatives=["A", "B", "D", "E", "G", "H"],
)
ps = [0.50, 0.60, 0.70, 0.80, 0.89, 0.89, 0.89, 0.94, 1]
qs = [0.50, 0.40, 0.30, 0.20, 0.10, 0.08, 0.05, 0.05, 0]
scaler = SumScaler("both")
dm = scaler.transform(dm)
kernels_len = []
for p, q in zip(ps, qs):
kselector = ELECTRE1(p=p, q=q)
dec = kselector.evaluate(dm)
klen = len(dec.kernel_)
if kernels_len and klen < kernels_len[-1]:
pytest.fail("less sensitive electre must have more "
f"alternatives in kernel. {kernels_len}")
kernels_len.append(klen)
def test_SumScaler_weights(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,
objectives=dm.objectives,
weights=dm.weights / np.sum(dm.weights),
alternatives=dm.alternatives,
criteria=dm.criteria,
dtypes=dm.dtypes,
)
scaler = SumScaler(target="weights")
result = scaler.transform(dm)
assert result.equals(expected)