Object oriented design in Python

Object oriented programming (OOP)

Benefits of object oriented design:

• Encapsulation
• Code reuse (composition, inheritance)
• Abstraction

OOP terminology

Term	Meaning	Example
Class	Blueprint for objects	class Location: ...
Instance	Concrete object created from a class	loc = Location("Antwerp")
Instance variable	Data stored per object	self.name = name
Attribute	Anything accessed with dot notation	loc.name, loc.plot()
Method	Function defined inside a class	def plot(self): ...
Property	Managed attribute using @property	@property def name(self): ...
Encapsulation	Hide internal details; provide safe interface	Use _var + property

Encapsulation

class Location:
    def __init__(self, name, longitude, latitude):
        self.name = name.upper() # Names are always uppercase
        self.longitude = longitude
        self.latitude = latitude

>>> loc = Location("Antwerp", 4.42, 51.22)
>>> loc.name
'ANTWERP'
>>> loc.name = "Antwerpen"
>>> loc.name
"Antwerpen" 😟

Encapsulation - Managed attributes

class Location:
    def __init__(self, name, longitude, latitude):
        self._name = name.upper() # Names are always uppercase
        ...

    @property
    def name(self):
        return self._name

    @name.setter
    def name(self, value):
        self._name = value.upper()

>>> loc = Location("Antwerp", 4.42, 51.22)
>>> loc.name = "Antwerpen"
>>> loc.name
"ANTWERPEN" 😊

Composition

Composition in object oriented design is a way to combine objects or data types into more complex objects.

classDiagram

    class Grid{
        + nx
        + dx
        + ny
        + dy
        + find_index()
    }

    class ItemInfo{
        + name
        + type
        + unit
    }

    class DataArray{
        + data
        + time
        + item
        + geometry
        + plot()
    }

    DataArray --* Grid
    DataArray --* ItemInfo

Composition - Example

class Grid:
    def __init__(self, nx, dx, ny, dy):
        self.nx = nx
        self.dx = dx
        self.ny = ny
        self.dy = dy
    
    def find_index(self, x,y):
        ...

class DataArray:
    def __init__(self, data, time, item, geometry):
        self.data = data
        self.time = time
        self.item = item
        self.geometry = geometry

    def plot(self):
        ...

DataArray has a geometry (e.g. Grid) and an item (ItemInfo).

Inheritance

• Inheritance is a way to reuse code and specialize behavior.
• A child class inherits the attributes and methods from the parent class.
• A child class can override the methods of the parent class.
• A child class can add new methods.

Inheritance - Example

classDiagram
    class Shape {
        +color
        +describe()
        +area()*
    }

    class Circle {
        +radius
        +area()
    }

    class Rectangle {
        +width
        +height
        +area()
    }

    Shape <|-- Circle
    Shape <|-- Rectangle

import math

class Shape:
    def __init__(self, color="black"):
        self.color = color

    def describe(self):
        return f"A {self.color} shape with area = {self.area():.2f}"

    def area(self):
        raise NotImplementedError

class Circle(Shape):
    def __init__(self, radius, color="black"):
        super().__init__(color)
        self.radius = radius

    def area(self):
        return math.pi * self.radius ** 2

class Rectangle(Shape):
    def __init__(self, width, height, color="black"):
        super().__init__(color)
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height

shapes = [
    Circle(1.0, color="red"),
    Rectangle(2.0, 3.0, color="blue")
]
for shape in shapes:
    print(shape.describe())

A red shape with area = 3.14
A blue shape with area = 6.00

Composition vs inheritance

• Composition: a car has a wheel.

• Inheritance: a sports car is a car.

• Inheritance is used to specialize behavior.
• In most cases, composition is a better choice than inheritance.
• Some recent programming languages (e.g. Go & Rust) do not support this style of inheritance.
• Use inheritance only when it makes sense.

Abstraction

Version A

total = 0.0
for x in values:
    total = total +x

Version B

total = sum(values)

• Using functions, e.g. sum() allows us to operate on a higher level of abstraction.
• Too little abstraction will force you to write many lines of boiler-plate code
• Too much abstraction limits the flexibility
• ✨Find the right level of abstraction!✨

• Which version is easiest to understand?
• Which version is easiest to change?

Collections Abstract Base Classes

classDiagram
    Container <|-- Collection
    Sized <|-- Collection
    Iterable <|-- Collection
   
    class Container{
        __contains__(self, x)
    }

    class Sized{
        __len__(self)
    }

    class Iterable{
        __iter__(self)
    }

• If a class implements __len__ it is a Sized object.
• If a class implements __contains__ it is a Container object.
• If a class implements __iter__ it is a Iterable object.

Collections Abstract Base Classes

>>> a = [1, 2, 3]
>>> 1 in a
True
>>> a.__contains__(1)
True
>>> len(a)
3
>>> a.__len__()
3
>>> for x in a:
...     v.append(x)
>>> it = a.__iter__()
>>> next(it)
1
>>> next(it)
2
>>> next(it)
3
>>> next(it)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration

Collections Abstract Base Classes

classDiagram
    Container <|-- Collection
    Sized <|-- Collection
    Iterable <|-- Collection
    Collection <|-- Sequence
    Collection <|-- Set
    Sequence <|-- MutableSequence
    Mapping <|-- MutableMapping
    Collection <|-- Mapping

    MutableSequence <|-- List
    Sequence <|-- Tuple
    MutableMapping <|-- Dict

Pythonic

If you want your code to be Pythonic, you have to be familiar with these types and their methods.

Dundermethods:

• __getitem__
• __setitem__
• __len__
• __contains__
• …

class JavaLikeToolbox:

    def __init__(self, tools: Collection[Tool]) -> None:
        self.tools = tools
    
    def getToolByName(self, name: str) -> Tool:
        for tool in self.tools:
            if tool.name == name:
                return tool

    def numberOfTools(self) -> int:
        return len(self.tools)

tb = JavaLikeToolbox([Hammer(), Screwdriver()])
tb.getToolByName("hammer")

<__main__.Hammer at 0x7f64246eda90>

tb.numberOfTools()

2

class Toolbox:

    def __init__(self, tools: Collection[Tool]):
        self._tools = {tool.name: tool for tool in tools}

    def __getitem__(self, name: str) -> Tool:
        return self._tools[name]
    
    def __len__(self) -> int:
        return len(self._tools)

tb = Toolbox([Hammer(), Screwdriver()])
tb["hammer"]

<__main__.Hammer at 0x7f64247047d0>

len(tb)

2

You want your code to feel like the built-in types.

class SparseMatrix:
    def __init__(self, shape, fill_value=0.0, data=None):
        self.shape = shape
        self._data = data if data is not None else {}
        self.fill_value = fill_value
        
    def __setitem__(self, key, value):
        i,j = key
        self._data[i,j] = float(value) 

    def __getitem__(self, key) -> float:
        i,j = key
        return self._data.get((i,j), self.fill_value)
    
    def transpose(self) -> "SparseMatrix":
        data = {(j,i) : v for (i,j),v in self._data.items()}
        return SparseMatrix(data=data,
                            shape=self.shape,
                            fill_value=self.fill_value)
    
    def __repr__(self):
        matrix_str = ""
        for j in range(self.shape[1]):
            for i in range(self.shape[0]):
                value = self[i, j]
                matrix_str += f"{value:<4}"
            matrix_str += "\n"
        return matrix_str

m = SparseMatrix(shape=(2,2), fill_value=0.0)
m

0.0 0.0 
0.0 0.0 

m[0,1]

0.0

m[0,1] = 1.0
m[1,0] = 2.0
m

0.0 2.0 
1.0 0.0 

m.transpose()

0.0 1.0 
2.0 0.0 

Duck typing

• “If it walks like a duck and quacks like a duck, it’s a duck”
• From the perspective of the caller, it doesn’t matter if it is a rubber duck or a real duck.
• The type of the object is not important, as long as it has the right methods.
• Python is different than C# or Java, where you would have to create an interface IToolbox and implement it for Toolbox.

import math

class Circle: # no base class
    def __init__(self, radius, color="black"):
        self.radius = radius
        self.color = color
    
    def area(self):
        return math.pi * self.radius**2
    
    def describe(self):
        return f"A {self.color} circle with area = {self.area():.2f}"

class Rectangle: # no base class
    def __init__(self, width, height, color="black"):
        self.width = width
        self.height = height
        self.color = color
    
    def area(self):
        return self.width * self.height
    
    def describe(self):
        return f"A {self.color} rectangle with area = {self.area():.2f}"

shapes = [
    Circle(1.0, color="red"),
    Rectangle(2.0, 3.0, color="blue")
]
for shape in shapes:
    print(shape.describe())

A red circle with area = 3.14
A blue rectangle with area = 6.00

Duck typing - Example

An example is a Scikit learn transformers

• fit
• transform
• fit_transform

If you want to make a transformer compatible with sklearn, you have to implement these methods.

Duck typing - Example

class PositiveNumberTransformer:

    def fit(self, X, y=None):
        # no need to fit (still need to have the method!)
        return self

    def transform(self, X):
        return np.abs(X)

    def fit_transform(self, X, y=None):
        return self.fit(X, y).transform(X)

Duck typing - Mixins

We can inherit some behavior from sklearn.base.TransformerMixin

from sklearn.base import TransformerMixin

class RemoveOutliersTransformer(TransformerMixin):

    def __init__(self, lower_bound, upper_bound):
        self.lower_bound = lower_bound
        self.upper_bound = upper_bound
        self.lower_ = None
        self.upper_ = None

    def fit(self, X, y=None):
        self.lower_ = np.quantile(X, self.lower_bound)
        self.upper_ = np.quantile(X, self.upper_bound)

    def transform(self, X):
        return np.clip(X, self.lower_, self.upper_)

    # def fit_transform(self, X, y=None):
    # we get this for free, from TransformerMixin

Let’s revisit the (date) Interval

The Interval class represent an interval in time.

class Interval:
    def __init__(self, start, end):
        self.start = start
        self.end = end

    def __contains__(self, x):
        return self.start < x < self.end

>>> dr = Interval(date(2020, 1, 1), date(2020, 1, 31))

>>> date(2020,1,15) in dr
True
>>> date(1970,1,1) in dr
False

What if we want to make another type of interval, e.g. a interval of numbers \([1.0, 2.0]\)?

A number interval

class Interval:
    def __init__(self, start, end):
        self.start = start
        self.end = end

    def __contains__(self, x):
        return self.start < x < self.end
    
>>> interval = Interval(5, 10)

>>> 8 in interval
True
>>> 12 in interval
False

As long as the start, end and x are comparable, the Interval class is a generic class able to handle integers, floats, dates, datetimes, strings …

Postel’s law

a.k.a. the Robustness principle of software design

1. Be liberal in what you accept
2. Be conservative in what you send

def process(number: Union[int,str,float]) -> int:
    # make sure number is an int from now on
    number = int(number)

    result = number * 2
    return result   

The consumers of your package (future self), will be grateful if you are not overly restricitive in what types you accept as input.

Example - Pydantic

from pydantic import BaseModel
from datetime import date

class Sensor(BaseModel):
    name: str
    voltage: float
    install_date: date
    location: tuple[float, float]

s1 = Sensor(name="Sensor 1",
            voltage=3.3,
            install_date=date(2020, 1, 1),
            location=(4.42, 51.22))
s1

Sensor(name='Sensor 1', voltage=3.3, install_date=datetime.date(2020, 1, 1), location=(4.42, 51.22))

data = {
    "name": "Sensor 1",
    "voltage": "3.3",
    "install_date": "2020-01-01",
    "location": ("4.42", "51.22")
}

s2 = Sensor(**data)
s2

Sensor(name='Sensor 1', voltage=3.3, install_date=datetime.date(2020, 1, 1), location=(4.42, 51.22))

Refactoring

• Refactoring is a way to improve the design of existing code
• Changing a software system in such a way that it does not alter the external behavior of the code, yet improves its internal structure
• Refactoring is a way to make code more readable and maintainable
• Housekeeping

Common refactoring techniques:

• Extract method
• Extract variable
• Rename method
• Rename variable
• Rename class
• Inline method
• Inline variable
• Inline class

Rename variable

Before

n = 0
for v in y:
    if v < 0:
        n = n + 1

After

FREEZING_POINT = 0.0
n_freezing_days = 0
for temp in daily_max_temperatures:
    if temp < FREEZING_POINT:
        n_freezing_days = n_freezing_days + 1 

Extract variable

Before

def predict(x):
    return min(0.0, 0.5 + 2.0 * min(0,x) + (random.random() - 0.5) / 10.0)

After

def predict(x):
    scale = 10.0
    error = (random.random() - 0.5) / scale
    a = 0.5
    b = 2.0 
    draft = a + b * x + error
    return min(0.0, draft)

Extract method

def error(scale):
    return (random.random() - 0.5) / scale

def linear_model(x, *, a=0.0, b=1.0):
    return a + b * x

def clip(x, *, min_value=0.0):
    return min(min_value, x)

def predict(x): 
    draft = linear_model(x, a=0.5, b=2.0) + error(scale=10.0)
    return clip(draft, min_value=0.)

Inline method

Opposite of extract mehtod.

def predict(x): 
    draft = linear_model(x, a=0.5, b=2.0) + error(scale=10.0)
    return min(0.0, x)

Composed method

Break up a long method into smaller methods.

# get data
os.shutil.copyfile(thisfile, localfile)
df = read_csv(localfile)

# clean data
df.dropna()
df.drop_duplicates()
df[somevar<0.0] = 0.0

# transform data
df.date = pd.to_datetime(df.date) - 86400

# predict
predictions = df.height + df.weight * df.age

def get_data(filename,...):
    ...

def clean_data(df):
    ...

def transform_data(df):
    ...

def predict(df):
    ...

def main():
    df = get_data("raw_data.csv")
    cleaned_data = clean_data(df)
    final_data = transform_data(cleaned_data)
    predictions = predict(final_data)

Composed method

• Divide your program into methods that perform one identifiable task
• Keep all of the operations in a method at the same level of abstraction.
• This will naturally result in programs with many small methods, each a few lines long.
• When you use Extract method a bunch of times on a method the original method becomes a Composed method.

If you want to learn more about refactoring, I recommend the book “Refactoring: Improving the Design of Existing Code” by Martin Fowler.

Summary

• OOP helps organize code into classes and objects
• Key principles: encapsulation, composition, inheritance, abstraction
• Duck typing – focus on behavior, not type
• Postel’s Law – be liberal in what you accept, strict in what you return
• Refactoring – improve design without changing behavior