Abstract base classes and how to use them in your data science project

Abstract base classes and how to use them in your data science project

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.

Inheritance

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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