The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
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[x] Class names and argument / API variables are very clear: there is no possible ambiguity. They also respect the existing code style (avoid duplicating words for the same concept) and are intuitive.
[x] Use typing like variable: Typing = ... as much as possible. Also use typing for function arguments and return values like def my_func(self, my_list: Dict[int, List[str]]) -> 'OrderedDict[int, str]':.
[x] Classes are documented: their behavior is explained beyond just the title of the class. You may even use the description written in your pull request above to fill some docstrings accurately.
[x] If a numpy array is used, it is important to remember that these arrays are a special type that must be documented accordingly, and that numpy array should not be abused. This is because Neuraxle is a library that is not only limited to transforming numpy arrays. To this effect, numpy steps should probably be located in the existing numpy python files as much as possible, and not be all over the place. The same applies to Pandas DataFrames.
[x] Code coverage is above 90% for the added code for the unit tests.
[x] The above description of the pull request in natural language was used to document the new code inside the code's docstrings so as to have complete documentation, with examples.
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[x] Code files that were edited were reformatted automatically using PyCharm's Ctrl+Alt+L shortcut. You may have reorganized imports as well.
Improve services parallelization
What it is
This pull request allows:
How it works
Behavior unchanged except that some repositories now have a wrapper and that the context does not have a global lock anymore, which is good.
Checklist before merging PR.
Things to check each time you contribute:
variable: Typing = ...as much as possible. Also use typing for function arguments and return values likedef my_func(self, my_list: Dict[int, List[str]]) -> 'OrderedDict[int, str]':.Ctrl+Alt+Lshortcut. You may have reorganized imports as well.