Abstract base classes and how to use them in your data science project. Write cleaner and safer python code by using this object-oriented programming building block. I will also share a little trick that you can use to automate unit testing across your classes.
## This code snippet was taken from sci-kit learn and modified for illustrative purposes ## Author: Jan Hendrik Metzen <[email protected]> ## License: BSD 3 clause ## Modified by Sebastian Dick, 2020 from abc import ABC, abstractmethod class Kernel(ABC): @abstractmethod def __call__(self, X, Y=None, eval_gradient=False): """Evaluate the kernel.""" @abstractmethod def is_stationary(self): """Returns whether the kernel is stationary. """ class RBF(Kernel): class DotProduct(Kernel):
Have you ever encountered code like this and wondered what’s so *abstract *about these classes and methods? Well, I’m about to show you!
If you stick around long enough I will also share a little trick that you can use to** automate unit testing** across your classes.
One key concept of object-oriented programming (OOP) is inheritance. Inheritance means that we base one (child-)class on another (parent-)class. The child class thereby inherits certain properties from its parent while at the same time implementing new ones.
Let’s look at an example. Imagine you want to write a software library about the animal world. You would perhaps start by defining a class Animal:
class Animal: def __init__(self, height, weight): self.height = height self.weight = weight
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