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gpyrn's Issues

Python version and scipy dependency

Due to scipy's requirements we are "stuck" with python = ">=3.8, <3.11"

>The current project's Python requirement (>3.0,<3.11) is not compatible with some of the required packages Python requirement:
    - scipy requires Python >=3.8,<3.11, so it will not be satisfied for Python >3.0,<3.8
  
  Because no versions of scipy match >1.8.0,<2.0.0
   and scipy (1.8.0) requires Python >=3.8,<3.11, scipy is forbidden.
  So, because gpyrn depends on scipy (^1.8.0), version solving failed.

  at ~/.local/share/pypoetry/venv/lib/python3.8/site-packages/poetry/puzzle/solver.py:241 in _solve
      237│             packages = result.packages
      238│         except OverrideNeeded as e:
      239│             return self.solve_in_compatibility_mode(e.overrides, use_latest=use_latest)
      240│         except SolveFailure as e:
    → 241│             raise SolverProblemError(e)
      242│ 
      243│         results = dict(
      244│             depth_first_search(
      245│                 PackageNode(self._package, packages), aggregate_package_nodes

  • Check your dependencies Python requirement: The Python requirement can be specified via the `python` or `markers` properties
    
    For scipy, a possible solution would be to set the `python` property to ">=3.8,<3.11"

    https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
    https://python-poetry.org/docs/dependency-specification/#using-environment-markers

Should we go with more recent python versions or should we search for a scipy version that allows to use older python versions @j-faria ?

No Weighting ?

Hello ! It's me again :)

I just wanted to know if there is a way to not use weighting just as we do for standard Gaussian Process Regression in the case of a single dataset ?

I mean, this is possible by using a constant and fixed weighting but my question is can we use no weighting at all?

Cheers.
Paul

Optimizing hyperparameters using several datasets

Hello João ! Hello Team !

Wonderful work.

I just wanted to know how do you optimize the hyperparameters when using several datasets with MCMC or even with the default optimization when doing gprn.optimize ? I've tried to mimic the way it used to be with a single parameter but I didn't succeed.

Cheers !
Paul.

PS: I really think this could be very useful for my research so I will be very interested in having a discussion with you guys or even to collaborate.

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