♻️ Move optimization to new module glotaran.optimization

[joernweissenborn]

Change summary

• created new module optimization
• deprecated glotaran.analysis.optimize
• adapted all imports

Checklist

• ✔️ Passing the tests (mandatory for all PR's)
• 🚧 Added changes to changelog (mandatory for all PR's)

Closes issues

None.

[github-actions]

👈 Launch a binder notebook on branch joernweissenborn/pyglotaran/refactor/optimization

[codecov]

Codecov Report

Attention: Patch coverage is 96.55172% with 1 line in your changes missing coverage. Please review.

Project coverage is 87.1%. Comparing base (96e6861) to head (2d9a6df).
Report is 236 commits behind head on main.

Files	Patch %	Lines
glotaran/optimization/optimization_group.py	85.7%	1 Missing ⚠️

Additional details and impacted files

@@          Coverage Diff          @@
##            main   #1047   +/-   ##
=====================================
  Coverage   87.1%   87.1%           
=====================================
  Files        102     102           
  Lines       5381    5382    +1     
  Branches     995     995           
=====================================
+ Hits        4691    4692    +1     
  Misses       536     536           
  Partials     154     154           

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[sonarqubecloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
16 Code Smells

No Coverage information
1.4% Duplication

[s-weigand]

In theory, we would need to deprecate all the moved modules not just optimize.
But since optimize contains the only user facing API and the rest is internal API only I think it would be overkill to deprecate them all.

LGTM 👍

[github-actions]

Benchmark is done. Checkout the benchmark result page.
Benchmark differences below 5% might be due to CI noise.

Benchmark diff v0.5.1 vs. main

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [96b42630]       [2d9a6df1]
     <v0.5.1>                   
         76.1±2ms         77.7±2ms     1.02  BenchmarkOptimize.time_optimize(False, False, False)
        84.6±40ms         110±30ms    ~1.30  BenchmarkOptimize.time_optimize(False, False, True)
         74.2±1ms         81.9±3ms    ~1.10  BenchmarkOptimize.time_optimize(False, True, False)
         150±40ms         157±40ms     1.05  BenchmarkOptimize.time_optimize(False, True, True)
         95.3±2ms       94.3±0.8ms     0.99  BenchmarkOptimize.time_optimize(True, False, False)
          102±2ms          104±3ms     1.02  BenchmarkOptimize.time_optimize(True, False, True)
         94.0±2ms       93.7±0.7ms     1.00  BenchmarkOptimize.time_optimize(True, True, False)
         102±20ms         105±40ms     1.02  BenchmarkOptimize.time_optimize(True, True, True)
             203M             205M     1.01  IntegrationTwoDatasets.peakmem_optimize
       2.49±0.09s       2.36±0.04s     0.95  IntegrationTwoDatasets.time_optimize

Benchmark diff main vs. PR

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [96e6861f]       [2d9a6df1]
         81.3±2ms         77.7±2ms     0.96  BenchmarkOptimize.time_optimize(False, False, False)
         135±30ms         110±30ms    ~0.82  BenchmarkOptimize.time_optimize(False, False, True)
         76.9±2ms         81.9±3ms     1.07  BenchmarkOptimize.time_optimize(False, True, False)
         109±30ms         157±40ms    ~1.45  BenchmarkOptimize.time_optimize(False, True, True)
         97.9±2ms       94.3±0.8ms     0.96  BenchmarkOptimize.time_optimize(True, False, False)
          102±4ms          104±3ms     1.02  BenchmarkOptimize.time_optimize(True, False, True)
       92.9±0.7ms       93.7±0.7ms     1.01  BenchmarkOptimize.time_optimize(True, True, False)
         106±40ms         105±40ms     0.98  BenchmarkOptimize.time_optimize(True, True, True)
             208M             205M     0.98  IntegrationTwoDatasets.peakmem_optimize
       2.26±0.05s       2.36±0.04s     1.05  IntegrationTwoDatasets.time_optimize