79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
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"""decision 包测试。"""
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from __future__ import annotations
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import numpy as np
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import pytest
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pytestmark = pytest.mark.skip(reason="未实现：见 docs/03-roadmap.md L1")
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def test_topsis_ranks_dominated_alternative_last():
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"""一个在所有指标上都最差的方案，TOPSIS 必须排最后。"""
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from mcm_toolkit.decision import topsis
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X = np.array(
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[
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[9.0, 8.0, 7.0], # 全面最优
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[5.0, 5.0, 5.0], # 中等
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[1.0, 1.0, 1.0], # 全面最差
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]
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)
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r = topsis(X, benefit_columns=[0, 1, 2])
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ranks = dict(zip(range(3), r.data["rank"], strict=False))
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assert ranks[0] == 1
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assert ranks[2] == 3
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def test_topsis_direction_flips_result():
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"""把某列从效益型改成成本型，排名必须变——方向不能静默搞错。"""
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from mcm_toolkit.decision import topsis
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X = np.array([[9.0, 1.0], [1.0, 9.0]])
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a = topsis(X, benefit_columns=[0, 1])
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b = topsis(X, benefit_columns=[0])
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assert list(a.data["rank"]) != list(b.data["rank"])
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def test_entropy_weights_sum_to_one_and_prefers_dispersed_column(rng):
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from mcm_toolkit.decision import entropy_weights
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X = np.column_stack([rng.normal(size=50), np.ones(50)]) # 第二列无差异
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r = entropy_weights(X)
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assert np.isclose(r.params["weights"].sum(), 1.0)
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assert r.params["weights"][0] > r.params["weights"][1]
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def test_entropy_weights_handles_zero_and_negative():
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"""熵权法的三个坑之一：零值不能产生 NaN。"""
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from mcm_toolkit.decision import entropy_weights
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r = entropy_weights(np.array([[0.0, 1.0], [1.0, 2.0], [2.0, 0.0]]))
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assert np.isfinite(r.params["weights"]).all()
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def test_ahp_consistency_for_consistent_matrix():
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"""完全一致的判断矩阵 CR 应接近 0。"""
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from mcm_toolkit.decision import ahp_weights
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A = np.array([[1, 2, 4], [0.5, 1, 2], [0.25, 0.5, 1]], dtype=float)
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r = ahp_weights(A)
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assert r.metrics["CR"] < 0.01
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def test_ahp_weights_match_geometric_mean_for_consistent_matrix():
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from mcm_toolkit.decision import ahp_weights
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A = np.array([[1, 2, 4], [0.5, 1, 2], [0.25, 0.5, 1]], dtype=float)
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r = ahp_weights(A)
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geo = (A.prod(axis=1)) ** (1 / 3)
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assert np.allclose(r.params["weights"], geo / geo.sum(), atol=1e-3)
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def test_gm11_fits_exponential_sequence():
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"""GM(1,1) 对纯指数序列应拟合得极好。"""
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from mcm_toolkit.decision import gm11_fit_predict
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series = [2.0 * 1.1**k for k in range(6)]
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r = gm11_fit_predict(series, steps=2)
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assert r.metrics["c_ratio"] < 0.35 # 后验差一级
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