Add initial patience & delta mode to early_stopping

[JamesAllingham]

(Mostly) addresses #11.

The main changes are:

• Fix min_delta being ignored by the early_stopping callback.
• Add a delta_mode option to early stopping that allows min_delta to either be interpreted as a "absolute" or "relative" difference.
• Add an initial_patience option, which is the same as patience but only considers the first n steps. This is useful when the initial stages of training are very noisy, etc. It is similar to start_from_epoch from Keras.
• Renamed existing "mode" to "optimization_mode" for clarity. (Also in the checkpoint callback, for consistency).
• Added tests.

This doesn't address the problem that the best weights are only returned if the patience has run out. After looking more closely at the code, I think that fixing it requires a more considerable refactoring of the code, so I'll make another issue to discuss. In short, I would like to upgrade the callback functionality such that each callback can have multiple callback functions. E.g., the early_stopping callback could have a generic __loop_callback__ that is called on the schedule specified by the user, and a on_epoch_end that is called at the end of training. This seems to be how the Keras callbacks work, and as far as I understand, it isn't possible with the current setup. But more on that later, let me know what you think of these changes!

[codecov]

Codecov Report

Merging #14 (9270181) into main (f39f265) will increase coverage by 0.49%.
Report is 1 commits behind head on main.
The diff coverage is 93.75%.

@@            Coverage Diff             @@
##             main      #14      +/-   ##
==========================================
+ Coverage   78.39%   78.88%   +0.49%     
==========================================
  Files          12       12              
  Lines        1578     1596      +18     
==========================================
+ Hits         1237     1259      +22     
+ Misses        341      337       -4     

Files Changed	Coverage Δ
ciclo/loops/loop.py	86.46% <ø> (+2.25%)	⬆️
ciclo/callbacks.py	65.73% <93.54%> (+2.03%)	⬆️
ciclo/__init__.py	100.00% <100.00%> (ø)

... and 1 file with indirect coverage changes

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[JamesAllingham]

I used the same convention as the existing mode argument (now called optimization_mode) , i.e., using an Enum to represent the options. For consistency, perhaps it makes sense to define a similar Enum for the InnerLoopAggregation options introduced in #9? (Or we could use that PR's approach of defining a string Literal type for the different options?)

[cgarciae]

Thanks @JamesAllingham! Will review it soon :)