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Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
Dataclasses & What are they for
Dataclasses are an implementation of a code generator, similar to a Named Tuple.
The idea of using a code generator is to save time and reduce wordiness.
There are two views about the purpose of Dataclasses:
- It makes a mutable data holder, in the spirit of named tuples
- It writes boiler-plate code for you, simplifying the process of writing the class
These 2 world views are reflected in the name: “Dataclasses”
Per the dataclasses PEP, they are roughly a “Mutable named tuple with defaults”
They provide an elegant syntax for creating data holder objects
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A Complex Example of using Dataclasses
In the image example, the generated code includes:
- typed attributes
- hidden attributes from __repr__ response
- custom __hash__ dunder with the selected fields
- attribute with default factory value in __init__ constructor
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Generated Code Includes
- Dunder methods like init, repr, eq, lt, le, etc.
- Frozen nature by generating dunder methods for __setattr__ and __delattr__
- Class variables for the provided attributes
The code generation can be configured by providing keyword a...
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Comparison with Named Tuples
- Dataclasses are by default mutable
- Named Tuples have methods which start with an underscore which can be confusing. Ex: obj._replace...
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A History Lesson
In the beginning there where:
- dicts for look-ups
- tuples for structures
- hand-written classes for more functionality
Later Named Tuples where added as a code generator with the goal of adding ...
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I started using Dataclasses and I think you should too
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