perf: cache yield_stdev using spec and interpcodes of model

[lhenkelm]

This implements the changes to hashing discussed in #315.

It was necessary to modify one test that was directly looking up cached results in the yield_stdev cache.

* caching for yield uncertainty calculation now based on model specification and interpolation codes
* caching now works when re-creating pyhf.pdf.Model objects

[alexander-held]

Thanks a lot for implementing this! This is super useful to prevent accidental re-computation of computationally expensive results.

[codecov]

Codecov Report

Merging #322 (766573b) into master (40120c2) will not change coverage.
The diff coverage is 100.00%.

@@            Coverage Diff            @@
##            master      #322   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files           23        23           
  Lines         1881      1889    +8     
  Branches       305       306    +1     
=========================================
+ Hits          1881      1889    +8     

Impacted Files	Coverage Δ
src/cabinetry/model_utils.py	100.00% <100.00%> (ø)

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[alexander-held]

Thank you very much for the PR, this all looks good to me. I'd like to see whether we can get a resolution of scikit-hep/pyhf#1762 (comment), but as far as I can tell this has no impact for the equality comparison done here (where we care about equal expected_data output). Maybe worth adding another sentence to the docstring to prevent possible future confusion: the key ignores measurement config information, but as far as I can tell that matters for neither logpdf nor expected_data, and only for configuration information like config.suggested_bounds().

[lhenkelm]

Thanks for reviewing! I added the warning to the docstring.
In the specific case of yield_stdev I agree this makes no difference, since no fits will be done and a full fit result is passed in alongside the model. In general the best thing would be to have pyhf objects that are deliberate about how they hash (in so far as they model values), since capturing everything would involve diving deep into the tree of pyhf objects, many of them internal to pyhf.

[alexander-held]

Looks great, thanks again!