Refactor to simplify input/output descriptors and decorators

[dantegd]

This PR aims to refactor our descriptors and decorators to simplify them to make them significantly easier to test, debug and mantain.

[dantegd]

@betatim @wphicks @divyegala this reflects our POC after our initial discussions, will be applying them to a real estimator alongside finishing some todos so we can see the full design in action and discuss any necessary aspects remaining.

[betatim]

One weird thing while playing with this a bit: when I add a print(f"{self.coef_=}") here I get the following:

Traceback (most recent call last):
  File "/home/coder/cuml/../ff.py", line 9, in <module>
    e.fit(X, y)
  File "/home/coder/.conda/envs/rapids/lib/python3.12/site-packages/cuml/internals/api_decorators.py", line 190, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/coder/.conda/envs/rapids/lib/python3.12/site-packages/cuml/sample/estimator.py", line 32, in wrapper
    result = func(self, *args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/coder/.conda/envs/rapids/lib/python3.12/site-packages/cuml/sample/estimator.py", line 115, in fit
    print(f"{self.coef_=}")
             ^^^^^^^^^^
  File "base.pyx", line 337, in cuml.internals.base.Base.__getattr__
AttributeError: coef_. Did you mean: '_coef_'?

I have stared at this for quite a while but can't work out what is going on??

[betatim]

Realised about 2min after leaving the office: it is because _is_fitted doesn't get set until fit returns. Maybe something to improve as it makes for a tedious to debug thing :D - I'll ponder a suggestion

[dantegd]

That is the same behavior a scikit-learn, no?

[betatim]

I don't think so. I was trying to access the coef_ attribute within fit.

In scikit-learn these are normal attributes, so once they are set you can use them. Right now we define __getattribute__ which uses _is_fit. I think it is a bit weird to have code like this fail, mostly because it makes you question your sanity and because the exception doesn't contain a clue (we get to see the AttributeError from __getattr__ not __getattribute__ :():

self.foo_ = 42
print(self.foo_) # Error, `foo_` doesn't exist!

Maybe we can get around the need to checking _is_fit and using __getattribute__ by recording inside the DynamicDescriptor if it has been set or not:

class DynamicDescriptor:
  def __init__(self, attribute_name):
    self.set = False
    self.attribute_name = f"{name}"

  def __get__(self, obj, objtype=None):
    if obj is None:
       return self
    if not self.set:
      raise AttributeError(f"{obj.__class__.__name__} object has no attribute {self.attribute_name}")
    else:
      if GlobalSettings().is_internal
        return self.raw
      else:
        return self.raw.to_output(obj._input_type)

  def __set__(self, obj, value):
    self.set = True
    # we can even store the value inside the descriptor?!
    self.raw = value

This might need a bit of tweaking to make the message in the exception look right ("'Estimator' object has no attribute 'foo_'").