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[Pyomo.DoE] update documentation #3812
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Member
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Same comment here about the top row. Something seems off in these results. |
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Member
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Same comment here about the top row. Something seems off in these results. |
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Member
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Same comment here about the top row. Something seems off in these results. |
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Why is does the top row of this plot (T=300) look strange? The optimality criteria is positive there but negative everywhere else? The previous plots from this example didn't have this artifact.
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That top row at T=300K represents the worst design in the space. Because we are minimizing the A-optimality criteria, the maximum positive value is highly unfavorable. Physically, this makes perfect sense: the example uses the Arrhenius equation, so at a low temperature like 300K, the reaction rate drops significantly, yielding very little information for the design.
Regarding the difference from older plots: since this is strictly a documentation PR, I haven't modified any underlying model parameters. I simply generated this updated plot using the current state of the codebase. The previous plots in the docs were likely generated using an older version of the model before recent updates to the main branch. I see that the
.pngfile indoe.rstwas last changed 1year ago, but thedoe/examples/result.jsonwhich is used in the example script was changed 7 months ago, and there was a change incontrol_points. So my understanding is that this or something else was not captured in the stable documentation. Finally, the 'sudden' visual jump at 300K is just a discretization artifact; the transition would look much smoother if we plotted this over a finer temperature grid rather than the current coarse steps.There was a problem hiding this comment.
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Could you run a quick test to confirm this? Could you include both 300K and 305K in the heatmap and paste the figure here?
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The plotting function uses
np.linspace, so to make the computation quick, I have used the range of temperature from 300K to 350K and used 6 points. I think it will help with the idea that the jump in value is actually not a jump, but a gradual change.Uh oh!
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I have also added points from 300K -> 305K; this will make it clearer.