Dynamic types in Python.

I’m constantly learning new things about the Python language. I consider myself a pretty good python programmer but often you never need to use all of the language features when writing your own code. For example I’ve not used is the class factory pattern using the type built in function. I’ve been aware of class factories, and read a few blog posts but never grokked it, until now.

A class factory is a function or another class that can create classes at runtime rather than you writing out the class definition in code. In other words it’s a way to programatically create classes. Normally when you write python you create classes in your code like so:

class A:
    def __init__(self):
        self.x = 1
        self.y = 2

a = A()
print(a.x, a.y)

So above, we define the name of a class, A, and give it an initialization method. Then we create and instance of that class, a, and print its two instance variables. There is another way to create this class though using the type function, which lets you create types (classes). We could reconstruct the code above as follows:

def init_func(self):
    self.x = 1
    self.y = 2
A = type('A', (), {"__init__": init_func})

a = A()
print(a.x, a.y)

The fun bit is the third argument to type which defines a dictionary where the keys are the names of methods (and class variables) for the class. The values of the dictionary are either function names or values. Note that for init_func you don’t want to add the parentheses as this will call the function (and give an error). For me, this also helped illuminate why in python we have to pass self as the first argument to a method – because you can attach on any old function to a class. It also goes to show that self is just a convention. If you were so inclined you could make python look like another language that uses this and not self:

# Let's make python look a little more like C++
# which uses "this" instead of "self"
# Probably don't want to do this for real as it
# will really confuse people
def constructor(this):
    this.x = 1
    this.y = 2

B = type('Cpp', (), {'__init__': constructor})
b = B()
print(b.x, b.y)

So now lets use this knowledge to make our “factory”. Take most of the code above and wrap it in another function. This way you can pass configuration for your to-be-created class into the outer function. You can extend this as far as you want and don’t have to make it a function either, you could create a class whose job it is to create other classes – a class factory class!

def class_factory(name='A'):
    def init_func(self):
        self.x = 1
        self.y = 2
    return type(name, (), {"__init__": init_func})

A = class_factory()
a = A()
print(a.x, a.y)

This is all nice and abstract but how and why would you use this?

Lets have a look at the marshmallow library which performs object serialization. With marshmallow you can define incoming data, usually in the form of JSON and convert it to native python data types (and vice-versa) while validating the data conforms to a certain schema. Here is the example from their website:

from datetime import date
from pprint import pprint

from marshmallow import Schema, fields

class ArtistSchema(Schema):
    name = fields.Str()

class AlbumSchema(Schema):
    title = fields.Str()
    release_date = fields.Date()
    artist = fields.Nested(ArtistSchema())

bowie = dict(name="David Bowie")
album = dict(artist=bowie, title="Hunky Dory", release_date=date(1971, 12, 17))

schema = AlbumSchema()
result = schema.dump(album)
pprint(result, indent=2)
# { 'artist': {'name': 'David Bowie'},
#   'release_date': '1971-12-17',
#   'title': 'Hunky Dory'}

The key things to note are that you have to create schema classes that define fields that are of particular types. But what if you don’t know the types beforehand or the types changed outside of the control of your program? For example, you have a web service that accepts incoming data, but the input data is user defined (to a point) and the actual schema of the data are stored outside of your program. It’s not feasible for you to code in a new class in your web service every time the data changes but you also don’t want to accept any old user data. What you really need is to read in one of these external schemas to create a validator for the input data on the fly.

Class factories to the rescue! Lets see how we can build a marshmallow schema class from a simple definition stored in JSON as a dictionary of field names to types.

from typing import Dict
from marshmallow import Schema, fields

def schema_factory(schema_definition: Dict[str, str]):
        The schema definition is a simple dictionary with the name
        of the field as the key and a string that defines its type
        as the value. For example:
            'field1': 'str',
            'field2': 'date'
    fields = {}
    field_type_map = {'str': fields.Str,
        'date': fields.Date
        # add in other types as needed
    for schema_field_name, schema_field_value in schema_definition.items():
        # We find the right marshmallow field and initialize it.
        # Fields then defines the class variables that you would 
        # manually do when defining a marshmallow Schema.
        fields[schema_field_name] = field_type_map[schema_field_value]()
    return type('CustomSchema', (Schema,), fields)