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https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 01:18:52 -06:00
Use np.array for Variables.names
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@@ -22,7 +22,7 @@ from miplearn.solvers.tests import assert_equals
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def sample() -> Sample:
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sample = MemorySample(
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{
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"static_var_names": ["x[0]", "x[1]", "x[2]", "x[3]"],
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"static_var_names": np.array(["x[0]", "x[1]", "x[2]", "x[3]"], dtype="S"),
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"static_var_categories": ["default", None, "default", "default"],
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"mip_var_values": np.array([0.0, 1.0, 1.0, 0.0]),
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"static_instance_features": [5.0],
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@@ -112,10 +112,10 @@ def test_usage() -> None:
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def test_evaluate(sample: Sample) -> None:
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comp = PrimalSolutionComponent()
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comp.sample_predict = lambda _: { # type: ignore
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"x[0]": 1.0,
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"x[1]": 1.0,
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"x[2]": 0.0,
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"x[3]": None,
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b"x[0]": 1.0,
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b"x[1]": 1.0,
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b"x[2]": 0.0,
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b"x[3]": None,
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}
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ev = comp.sample_evaluate(None, sample)
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assert_equals(
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@@ -150,8 +150,8 @@ def test_predict(sample: Sample) -> None:
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assert_array_equal(x["default"], clf.predict_proba.call_args[0][0])
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assert_array_equal(x["default"], thr.predict.call_args[0][0])
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assert pred == {
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"x[0]": 0.0,
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"x[1]": None,
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"x[2]": None,
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"x[3]": 1.0,
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b"x[0]": 0.0,
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b"x[1]": None,
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b"x[2]": None,
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b"x[3]": 1.0,
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}
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