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ENH: Re-allow "locking" of models with first fit #120

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This allows having "frozen" models that are fit only once before entering the leave-one-out loop.

These models use all the data in the fitting and then return always the same "prediction".

This feature was lost with the refactor of the estimator. By moving it into the models, we can use them in a more flexible way.

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codecov bot commented May 14, 2025

Codecov Report

Attention: Patch coverage is 24.24242% with 25 lines in your changes missing coverage. Please review.

Project coverage is 33.30%. Comparing base (540ed9b) to head (4313fa4).
Report is 4 commits behind head on main.

Files with missing lines Patch % Lines
src/nifreeze/model/base.py 21.42% 11 Missing ⚠️
src/nifreeze/model/dmri.py 23.07% 10 Missing ⚠️
src/nifreeze/estimator.py 25.00% 3 Missing ⚠️
src/nifreeze/cli/run.py 0.00% 1 Missing ⚠️

❗ There is a different number of reports uploaded between BASE (540ed9b) and HEAD (4313fa4). Click for more details.

HEAD has 2 uploads less than BASE
Flag BASE (540ed9b) HEAD (4313fa4)
3 1
Additional details and impacted files
@@             Coverage Diff             @@
##             main     #120       +/-   ##
===========================================
- Coverage   71.04%   33.30%   -37.75%     
===========================================
  Files          22       23        +1     
  Lines        1036     1060       +24     
  Branches      121      128        +7     
===========================================
- Hits          736      353      -383     
- Misses        258      705      +447     
+ Partials       42        2       -40     

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@oesteban oesteban force-pushed the fix/reenable-single-fit-models branch from 4313fa4 to 5a9e00a Compare May 16, 2025 22:36
oesteban added 5 commits May 17, 2025 00:39
This allows having "frozen" models that are fit only once before
entering the leave-one-out loop.

These models use all the data in the fitting and then return always the
same "prediction".

This feature was lost with the refactor of the estimator. By moving it
into the models, we can use them in a more flexible way.
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