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Multiple dispatch in Python

Project description

Plum: Multiple Dispatch in Python

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Everybody likes multiple dispatch, just like everybody likes plums.

Installation

pip install plum-dispatch

Basic Usage

Multiple dispatch allows you to implement multiple methods for the same function, where the methods specify the types of their arguments:

from plum import dispatch

@dispatch(str)
def f(x):
    return 'This is a string!'
    

@dispatch(int)
def f(x):
    return 'This is an integer!'
>>> f('1')
'This is a string!'

>>> f(1)
'This is an integer!'

We haven't implemented a method for floats, so in that case an exception will be raised:

>>> f(1.0)
NotFoundLookupError: For function "f", signature (builtins.float) could not be resolved.

Instead of implementing a method for floats, let's implement a method for all numbers:

from numbers import Number


@dispatch(Number)
def f(x):
    return 'This is a number!'

Since a float is a Number, f(1.0) will return 'This is a number!'. But an int is also a Number, so f(1) can either return 'This is an integer!' or 'This is a number!'. The rule of multiple dispatch is that the most specific method is chosen:

>>> f(1)
'This is an integer!'

since an int is a Number, but a Number is not necessarily an int.

For an excellent and way more detailed overview of multiple dispatch, see the manual of the Julia Language.

Features by Example

Dispatch From Type Annotations

Dispatcher.annotations is an experimental feature that can be used to dispatch on a function's type annotations:

from plum import dispatch, add_conversion_method

add_conversion_method(type_from=int, type_to=str, f=str)


@dispatch.annotations()
def int_to_str(x: int) -> str:
    return x
    
    
@dispatch.annotations()
def int_to_str(x):
    raise ValueError('I only take integers!')
>>> int_to_str(1.0)
ValueError: I only take integers!

>>> int_to_str(1)
'1'

Union Types

Sets can be used to instantiate union types:

from plum import dispatch

@dispatch(object)
def f(x):
    print('fallback')


@dispatch({int, str})
def f(x):
    print('int or str')
>>> f(1)
int or str

>>> f('1')
int or str

>>> f(1.0)
fallback

Parametric Types

The parametric types Tuple and List can be used to dispatch on tuples and lists with particular types of elements. Importantly, the type system is covariant, as opposed to Julia's type system, which is invariant.

from plum import dispatch, Tuple, List

@dispatch({tuple, list})
def f(x):
    print('tuple or list')
    
    
@dispatch(Tuple(int))
def f(x):
    print('tuple of int')
    
    
@dispatch(List(int))
def f(x):
    print('list of int')
>>> f([1, 2])
'list of int'

>>> f([1, '2'])
'tuple or list'

>>> f((1, 2))
'tuple of int'

>>> f((1, '2'))
'tuple or list'

Variable Arguments

A list can be used to specify variable arguments:

from plum import dispatch

@dispatch(int)
def f(x):
    print('single argument')
    

@dispatch(int, [int])
def f(x, *xs):
    print('multiple arguments')
>>> f(1)
single argument

>>> f(1, 2)
multiple arguments

>>> f(1, 2, 3)
multiple arguments

Return Types

The keyword argument return_type can be set to specify return types:

from plum import dispatch, add_conversion_method

@dispatch({int, str}, return_type=int)
def f(x):
    return x
>>> f(1)
1

>>> f('1')
TypeError: Expected return type "builtins.int", but got type "builtins.str".

>>> add_conversion_method(type_from=str, type_to=int, f=int)

>>> f('1')
1

Inheritance

Since every class in Python can be subclassed, diagonal dispatch cannot be implemented. However, inheritance can be used to achieve a form of diagonal dispatch:

from plum import Dispatcher, Referentiable, Self

class Real(Referentiable):
    dispatch = Dispatcher(in_class=Self)

    @dispatch(Self)
    def __add__(self, other):
        return 'real'
        

class Rational(Real, Referentiable):
    dispatch = Dispatcher(in_class=Self)

    @dispatch(Self)
    def __add__(self, other):
        return 'rational'
        

real = Real()
rational = Rational()
>>> real + real
'real'

>>> real + rational
'real'

>>> rational + real
'real'

>>> rational + rational
'rational'

Conversion

The function convert can be used to convert objects of one type to another:

from numbers import Number

from plum import convert


class Rational(object):
    def __init__(self, num, denom):
        self.num = num
        self.denom = denom
>>> convert(0.5, Number)
0.5

>>> convert(Rational(1, 2), Number)
TypeError: Cannot convert a "__main__.Rational" to a "numbers.Number".

The TypeError indicates that convert does not know how to convert a Rational to a Number. Let us implement that conversion:

from operator import truediv

from plum import conversion_method
        

@conversion_method(type_from=Rational, type_to=Number)
def rational_to_number(q):
    return truediv(q.num, q.denom)
>>> convert(Rational(1, 2), Number)
0.5

Instead of the decorator conversion_method, one can also use add_conversion_method:

from plum import add_conversion_method

add_conversion_method(type_from, type_to, conversion_function)

Promotion

The function promote can be used to promote objects to a common type:

from plum import dispatch, promote, add_promotion_rule, add_conversion_method

@dispatch(object, object)
def add(x, y):
    return add(*promote(x, y))
    
    
@dispatch(int, int)
def add(x, y):
    return x + y
    
    
@dispatch(float, float)
def add(x, y):
    return x + y
>>> add(1, 2)
3

>>> add(1.0, 2.0)
3.0

>>> add(1, 2.0)
TypeError: No promotion rule for "builtins.int" and "builtins.float".

>>> add_promotion_rule(int, float, float)

>>> add(1, 2.0)
TypeError: Cannot convert a "builtins.int" to a "builtins.float".

>>> add_conversion_method(type_from=int, type_to=float, f=float)

>>> add(1, 2.0)
3.0

Method Precedence

The keyword argument precedence can be set to an integer value to specify precedence levels of methods, which are used to break ambiguity:

from plum import dispatch

class Element(object):
    pass


class ZeroElement(Element):
    pass


class SpecialisedElement(Element):
    pass


@dispatch(ZeroElement, Element)
def mul_no_precedence(a, b):
    return 'zero'


@dispatch(Element, SpecialisedElement)
def mul_no_precedence(a, b):
    return 'specialised operation'
    

@dispatch(ZeroElement, Element, precedence=1)
def mul(a, b):
    return 'zero'


@dispatch(Element, SpecialisedElement)
def mul(a, b):
    return 'specialised operation'
>>> zero = ZeroElement()

>>> specialised_element = SpecialisedElement()

>>> element = Element()

>>> mul(zero, element)
'zero'

>>> mul(element, specialised_element)
'specialised operation'

>>> mul_no_precedence(zero, specialised_element)
AmbiguousLookupError: For function "mul_no_precedence", signature (__main__.ZeroElement, __main__.SpecialisedElement) is ambiguous among the following:
  (__main__.ZeroElement, __main__.Element) (precedence: 0)
  (__main__.Element, __main__.SpecialisedElement) (precedence: 0)

>>> mul(zero, specialised_element)
'zero'

Parametric Classes

The decorator parametric can be used to create parametric classes:

from plum import dispatch, parametric

@parametric
class A(object):  # Must be a new-style class!
    pass
    
    
@dispatch(A)
def f(x):
    return 'fallback'
    
    
@dispatch(A(1))
def f(x):
    return '1'
    
    
@dispatch(A(2))
def f(x):
    return '2'
>>> A
__main__.A

>>> A(1)
__main__.A{1}

>>> issubclass(A(1), A)
True

>>> A(1)()
<__main__.A{1} at 0x10c2bab70>

>>> f(A(1)())
'1'

>>> f(A(2)())
'2'

>>> f(A(3)())
'fallback'

Add Multiple Methods

Dispatcher.multi can be used to implement multiple methods at once:

from plum import dispatch

@dispatch.multi((int, int), (float, float))
def add(x, y):
    return x + y
>>> add(1, 1)
2

>>> add(1.0, 1.0)
2.0

>>> add(1, 1.0)
NotFoundLookupError: For function "add", signature (builtins.int, builtins.float) could not be resolved.

Extend a Function From Another Package

Function.extend can be used to extend a particular function:

from package import f

@f.extend(int)
def f(x):
    return 'new behaviour'
>>> f(1.0)
'old behaviour'

>>> f(1)
'new behaviour'

Directly Invoke a Method

Function.invoke can be used to invoke a method given types of the arguments:

from plum import dispatch

@dispatch(int)
def f(x):
    return 'int'
    
    
@dispatch(str)
def f(x):
    return 'str'
>>> f(1)
'int'

>>> f('1')
'str'

>>> f.invoke(int)('1')
'int'

>>> f.invoke(str)(1)
'str'

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