REF: Refactor PET estimator

[jhlegarreta]

Refactor PET estimator so that it is more closely aligned with the Estimator class, and in order to eventually merge them.

[jhlegarreta]

I think this is close enough to the general estimator class.

I compared the current version and the one in the patch set:

• The one in the patch set is 6x faster (5:26 vs 32:10).
• Results are different, though.

Version the PETEstimator in main:

Using the PETEstimator in this patch set:

And none seems to be doing a good job: I have plot the ground truth:

Given that none is doing a good job, and unless you clearly see the fix for the original PETEstimator @mnoergaard, my opinion is that this should be merged so that we invest our time better fixing a version that is closer to the Estimator class so that merging both is eventually easier.

[codecov]

Codecov Report

❌ Patch coverage is 66.66667% with 15 lines in your changes missing coverage. Please review.
✅ Project coverage is 75.55%. Comparing base (4a96c3c) to head (c49905a).
⚠️ Report is 80 commits behind head on main.

Files with missing lines	Patch %	Lines
src/nifreeze/estimator.py	76.92%	7 Missing and 2 partials ⚠️
src/nifreeze/model/base.py	0.00%	6 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #203      +/-   ##
==========================================
- Coverage   75.95%   75.55%   -0.40%     
==========================================
  Files          24       24              
  Lines        1439     1432       -7     
  Branches      166      165       -1     
==========================================
- Hits         1093     1082      -11     
- Misses        275      280       +5     
+ Partials       71       70       -1     

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[oesteban]

@mnoergaard is this really what we want?

It can be done, but calling fit_predict(None) is meant to be done outside the iteration loop (i.e., fit only once in all data and after that fit_predict is only "predict").

Is there a reason to fit everytime (btw, in the case of diffusion models, if fit once with all data then the object is locked and doesn't fit again).

[oesteban]

Let's discuss this tomorrow @jhlegarreta