Pyomo.DoE: adding more verbose output for sensitivity analysis#3525
Pyomo.DoE: adding more verbose output for sensitivity analysis#3525mrmundt merged 77 commits intoPyomo:mainfrom
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@adowling2 @djlaky ready for early feedback |
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Next step:
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@smondal13 See #3532 for an example of what @jsiirola is suggesting |
…alue checking in compute_FIM_full_factorial()
…pute_FIM_metrics` function.
… doe object that uses the "reactor_experiment" example in doe directory. Also added the png files to gitignore that are stored when "reactor_example.py" is run.
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added print statement and changed a_tol of np.allclose for "log10 D-opt"
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@blnicho, I have added the print statement, and on my Mac, the values of "log10 D-opt" are exactly the same as the "log10_D_opt_expected" values. I have also solved the import issue. |
| self.assertTrue( | ||
| np.allclose(ff_results["log10 D-opt"], log10_D_opt_expected, atol=1e-6) | ||
| ) |
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Tests are still failing due to slight numerical differences across different operating systems. Try changing these to use self.assertStructuredAlmostEqual(value, expected) instead of the combo of assertTrue and np.allclose. I think this will print out which values are different when the test fails without needing the explicit print statement you added above. You may have to use Python lists of floats instead of numpy arrays and data types. Once we figure out the tolerance to use for "log10 D-opt", I'm expecting we'll see similar numerical issues with the checks below.
| self.assertTrue( | |
| np.allclose(ff_results["log10 D-opt"], log10_D_opt_expected, atol=1e-6) | |
| ) | |
| self.assertStructuredAlmostEqual(ff_results["log10 D-opt"], log10_D_opt_expected, abstol=1e-4) |
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@blnicho, I have changed the design_ranges for the example in test_doe_solve.py . Previously, the values of D-opt were very small. I have also changed the datatype of the lists. These changes will probably solve the numerical issue.
Co-authored-by: Bethany Nicholson <[email protected]>
…ged the expected values in class TestDoe from list of numpy floats to list of floats.
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@smondal13 the lint check failed. Please run |
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@blnicho, I have changed the example again. This time, it has passed all the checks, except one. |
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Fixes # .
Summary/Motivation:
get_labeled_model().reactor_example.pywill help with the testing.Changes proposed in this PR:
compute_FIM_full_factorial()check_FIM()in utils.py by separating it fromcheck_model_FIM()in doe.pycompute_FIM_metrics()in utils.pyget_FIM_metrics()in utils.pyreactor_example.pyLegal Acknowledgement
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