Handle uninitialized variable in propagate_solution of scaling transformation
#3275
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Coverage 88.56% 88.56%
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Files 877 877
Lines 99276 99282 +6
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+ Hits 87920 87927 +7
+ Misses 11356 11355 -1
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| value(scaled_v[k]) / component_scaling_factor_map[scaled_v[k]], | ||
| skip_validation=True, | ||
| ) | ||
| if scaled_v[k].value is not None: |
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Is the correct check here to test None, or to not transfer the values of any stale variables? I am wondering if the correct answer is to only propagate variable values when .stale==False
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I guess I can see three options:
- Propagate values in the model, unless they are None (what this PR currently does)
- Propagate values in the model, regardless of what they are
- Propagate non-stale values in the model
My instinct is that relying on the stale flag can lead to non-intuitive behavior, e.g. if a user sets a value after a solve (or solves via solve_strongly_connected_components), only the last values updated will be propagated. If this is what we decide on, the docstring should be updated to indicate that only the most recent solution is propagated.
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Based on @dallan-keylogic's comment on #2817, I've updated this PR to propagate values from the scaled model even if they are None, but to log a warning if None overrides a non-None value.
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I think I am OK with this behavior, but if we are always going to propagate the entire model state, I wonder if a better name for this method is propagate_state() (as we are propagating the current state of the model and not necessarily the last thing that came back from the solver).
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I agree that propagate_state, or something like propagate_variable_values, would be a better name, although I'm not convinced it's worth changing.
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This should fix #2817 as well. |
Fixes #3273, Fixes #2817
Changes proposed in this PR:
Nonein the scaled model, we don't attempt to set its value in the original modelLegal Acknowledgement
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