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Improve consistency in parameter names #257

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bellet opened this issue Oct 30, 2019 · 0 comments · Fixed by #324
Closed

Improve consistency in parameter names #257

bellet opened this issue Oct 30, 2019 · 0 comments · Fixed by #324

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@bellet
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bellet commented Oct 30, 2019 •
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It would be nice to follow a consistent naming convention for parameters and be as consistent as possible with sklearn. For instance:

  • In supervised versions of weakly supervised algorithms, num_constraints should be renamed n_constraints, num_chunks to n_chunks
  • In LMNN, the parameter k could be renamed n_neighbors like in sklearn's KNeighborsClassifier
  • There is also tol and convergence_threshold which are both used to refer to optimization tolerance (we should always use tol which is quite standard, cf `scipy.optimize)
@mvargas33 mvargas33 mentioned this issue Sep 14, 2021
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perimosocordiae pushed a commit that referenced this issue Jun 21, 2022
@mvargas33
Rename variables, proposed by issue #257 (#324)
17216a7 
* Rename number_constrains to n_constraints

* Renamed num_chunks to n_chunks

* LMNN k parameter renamed to n_neighbors

* Replaced all 'convergence_threshold' with 'tol'

* Fix tests

* Fixed more test regarding rename of variable

* Warnings for n_constrains

* Add all warnings regarding n_constrains

* Deprecation warnings for n_chunks

* Add deprecation warn to n_neighbors

* Add convergence_threshold warnings
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Rename variables, proposed by issue #257 mvargas33/metric-learn
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@bellet