pyomo.doe adding more verbose output for sensitivity analysis #3525
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,73 @@ | ||
| # ___________________________________________________________________________ | ||
| # | ||
| # Pyomo: Python Optimization Modeling Objects | ||
| # Copyright (c) 2008-2025 | ||
| # National Technology and Engineering Solutions of Sandia, LLC | ||
| # Under the terms of Contract DE-NA0003525 with National Technology and | ||
| # Engineering Solutions of Sandia, LLC, the U.S. Government retains certain | ||
| # rights in this software. | ||
| # This software is distributed under the 3-clause BSD License. | ||
| # ___________________________________________________________________________ | ||
| from pyomo.common.dependencies import ( | ||
| numpy as np, | ||
| numpy_available, | ||
| ) | ||
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| import pyomo.common.unittest as unittest | ||
| from pyomo.contrib.doe.doe import ( | ||
| _SMALL_TOLERANCE_IMG, | ||
| _compute_FIM_metrics, | ||
| ) | ||
|
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| @unittest.skipIf(not numpy_available, "Numpy is not available") | ||
| class TestComputeFIMMetrics(unittest.TestCase): | ||
| def test_compute_FIM_metrics(self): | ||
| # Create a sample Fisher Information Matrix (FIM) | ||
| FIM = np.array([[10, 2], [2, 3]]) | ||
|
|
||
| det_FIM, trace_FIM, E_vals, E_vecs, D_opt, A_opt, E_opt, ME_opt = ( | ||
| _compute_FIM_metrics(FIM) | ||
| ) | ||
|
|
||
| # expected results | ||
| det_expected = np.linalg.det(FIM) | ||
| D_opt_expected = np.log10(det_expected) | ||
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| trace_expected = np.trace(FIM) | ||
| A_opt_expected = np.log10(trace_expected) | ||
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| E_vals_expected, E_vecs_expected = np.linalg.eig(FIM) | ||
| min_eigval = np.min(E_vals_expected.real) | ||
| if min_eigval <= 0: | ||
| E_opt_expected = np.nan | ||
| else: | ||
| E_opt_expected = np.log10(min_eigval) | ||
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| cond_expected = np.linalg.cond(FIM) | ||
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| ME_opt_expected = np.log10(cond_expected) | ||
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| # Test results | ||
| self.assertEqual(det_FIM, det_expected) | ||
| self.assertEqual(trace_FIM, trace_expected) | ||
| self.assertTrue(np.allclose(E_vals, E_vals_expected)) | ||
| self.assertTrue(np.allclose(E_vecs, E_vecs_expected)) | ||
| self.assertEqual(D_opt, D_opt_expected) | ||
| self.assertEqual(A_opt, A_opt_expected) | ||
| self.assertEqual(E_opt, E_opt_expected) | ||
| self.assertEqual(ME_opt, ME_opt_expected) | ||
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| class TestFIMWarning(unittest.TestCase): | ||
| def test_FIM_eigenvalue_warning(self): | ||
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| # Create a matrix with an imaginary component large enough to trigger the warning | ||
| FIM = np.array([[6, 5j], [5j, 7]]) | ||
| with self.assertLogs("pyomo.contrib.doe", level="WARNING") as cm: | ||
| _compute_FIM_metrics(FIM) | ||
| expected_warning = f"Eigenvalue has imaginary component greater than {_SMALL_TOLERANCE_IMG}, contact developers if this issue persists." | ||
| self.assertIn(expected_warning, cm.output[0]) | ||
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| if __name__ == "__main__": | ||
| unittest.main() | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,69 @@ | ||
| # ___________________________________________________________________________ | ||
| # | ||
| # Pyomo: Python Optimization Modeling Objects | ||
| # Copyright (c) 2008-2025 | ||
| # National Technology and Engineering Solutions of Sandia, LLC | ||
| # Under the terms of Contract DE-NA0003525 with National Technology and | ||
| # Engineering Solutions of Sandia, LLC, the U.S. Government retains certain | ||
| # rights in this software. | ||
| # This software is distributed under the 3-clause BSD License. | ||
| # ___________________________________________________________________________ | ||
| from pyomo.common.dependencies import ( | ||
| numpy as np, | ||
| numpy_available, | ||
| ) | ||
|
|
||
| import pyomo.common.unittest as unittest | ||
| from pyomo.contrib.doe import DesignOfExperiments | ||
|
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||
| # Not need? from pyomo.contrib.doe.tests import doe_test_example | ||
| from pyomo.contrib.doe.doe import ( | ||
| _SMALL_TOLERANCE_DEFINITENESS, | ||
| _SMALL_TOLERANCE_SYMMETRY, | ||
| ) | ||
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| @unittest.skipIf(not numpy_available, "Numpy is not available") | ||
| class TestDesignOfExperimentsCheckFIM(unittest.TestCase): | ||
| """Test the check_FIM method of the DesignOfExperiments class.""" | ||
|
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||
| def test_check_FIM_valid(self): | ||
| """Test case where the FIM is valid (square, positive definite, symmetric).""" | ||
| FIM = np.array([[4, 1], [1, 3]]) | ||
| try: | ||
| # Call the static method directly | ||
| DesignOfExperiments._check_FIM(FIM) | ||
| except ValueError as e: | ||
| self.fail(f"Unexpected error: {e}") | ||
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| def test_check_FIM_non_square(self): | ||
| """Test case where the FIM is not square.""" | ||
| FIM = np.array([[4, 1], [1, 3], [2, 1]]) | ||
| with self.assertRaisesRegex(ValueError, "FIM must be a square matrix"): | ||
| DesignOfExperiments._check_FIM(FIM) | ||
|
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| def test_check_FIM_non_positive_definite(self): | ||
| """Test case where the FIM is not positive definite.""" | ||
| FIM = np.array([[1, 0], [0, -2]]) | ||
| with self.assertRaisesRegex( | ||
| ValueError, | ||
| r"FIM provided is not positive definite. It has one or more negative eigenvalue\(s\) less than -{:.1e}".format( | ||
| _SMALL_TOLERANCE_DEFINITENESS | ||
| ), | ||
| ): | ||
| DesignOfExperiments._check_FIM(FIM) | ||
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| def test_check_FIM_non_symmetric(self): | ||
| """Test case where the FIM is not symmetric.""" | ||
| FIM = np.array([[4, 1], [0, 3]]) | ||
| with self.assertRaisesRegex( | ||
| ValueError, | ||
| "FIM provided is not symmetric using absolute tolerance {}".format( | ||
| _SMALL_TOLERANCE_SYMMETRY | ||
| ), | ||
| ): | ||
| DesignOfExperiments._check_FIM(FIM) | ||
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| if __name__ == "__main__": | ||
| unittest.main() |
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Will you ever get to this warning? Or will _check_FIM throw an error first?
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@adowling2
_check_FIM()will not trigger this warning._check_FIM()only checks whether the FIM is square, symmetric, and positive definite. Although this method checks for the symmetry of the FIM with a tolerance, and it is unlikely that the FIM will have large imaginary eigenvalues, we have kept the warning there just in case there is a large imaginary eigenvalue.There was a problem hiding this comment.
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@smondal13 Thank you.
@blnicho @mrmundt Is there a best practice for testing this warning message is properly thrown?