https://github.com/jspahrsummers/adt
Algebraic data types for Python (experimental, not actively maintained)
https://github.com/jspahrsummers/adt
algebraic-data-types python3 sum-types tagged-unions
Last synced: 6 months ago
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Algebraic data types for Python (experimental, not actively maintained)
- Host: GitHub
- URL: https://github.com/jspahrsummers/adt
- Owner: jspahrsummers
- License: mit
- Created: 2019-06-26T20:48:24.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-05-25T12:26:33.000Z (over 4 years ago)
- Last Synced: 2024-04-27T02:43:50.124Z (over 1 year ago)
- Topics: algebraic-data-types, python3, sum-types, tagged-unions
- Language: Python
- Homepage:
- Size: 105 KB
- Stars: 168
- Watchers: 9
- Forks: 14
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# adt [](https://circleci.com/gh/jspahrsummers/adt)
`adt` is a library providing [algebraic data types](https://en.wikipedia.org/wiki/Algebraic_data_type) in Python, with a clean, intuitive syntax, and support for [`typing`](https://docs.python.org/3/library/typing.html) through a [mypy plugin](#mypy-plugin).
_**NOTE:** This project is experimental, and not actively maintained by the author. Contributions and forking are more than welcome._
**Table of contents:**
1. [What are algebraic data types?](#what-are-algebraic-data-types)
1. [Pattern matching](#pattern-matching)
1. [Compared to Enums](#compared-to-enums)
1. [Compared to inheritance](#compared-to-inheritance)
1. [Examples in other programming languages](#examples-in-other-programming-languages)
1. [Installation](#installation)
1. [mypy plugin](#mypy-plugin)
1. [Defining an ADT](#defining-an-adt)
1. [Generated functionality](#generated-functionality)
1. [Custom methods](#custom-methods)
# What are algebraic data types?
An [algebraic data type](https://en.wikipedia.org/wiki/Algebraic_data_type) (also known as an ADT) is a way to represent multiple variants of a single type, each of which can have some data associated with it. The idea is very similar to [tagged unions and sum types](https://en.wikipedia.org/wiki/Tagged_union), which in Python are represented as [Enums](#compared-to-enums).
ADTs are useful for a variety of data structures, including binary trees:
```python
@adt
class Tree:
EMPTY: Case
LEAF: Case[int]
NODE: Case["Tree", "Tree"]
```
Abstract syntax trees (like you might implement as part of a parser, compiler, or interpreter):
```python
@adt
class Expression:
LITERAL: Case[float]
UNARY_MINUS: Case["Expression"]
ADD: Case["Expression", "Expression"]
MINUS: Case["Expression", "Expression"]
MULTIPLY: Case["Expression", "Expression"]
DIVIDE: Case["Expression", "Expression"]
```
Or more generic versions of a variant type, like an `Either` type that represents a type A or a type B, but not both:
```python
L = TypeVar('L')
R = TypeVar('R')
@adt
class Either(Generic[L, R]):
LEFT: Case[L]
RIGHT: Case[R]
```
## Pattern matching
Now, defining a type isn't that interesting by itself. A lot of the expressivity of ADTs arises when you [pattern match](https://en.wikipedia.org/wiki/Pattern_matching) over them (sometimes known as "destructuring").
For example, we could use the `Either` ADT from above to implement a sort of error handling:
```python
# Defined in some other module, perhaps
def some_operation() -> Either[Exception, int]:
return Either.RIGHT(22) # Example of building a constructor
# Run some_operation, and handle the success or failure
default_value = 5
unpacked_result = some_operation().match(
# In this case, we're going to ignore any exception we receive
left=lambda ex: default_value,
right=lambda result: result)
```
_Aside: this is very similar to how error handling is implemented in languages like [Haskell](https://www.haskell.org/), because it avoids the unpredictable control flow of raising and catching exceptions, and ensures that callers need to make an explicit decision about what to do in an error case._
One can do the same thing with the `Expression` type above (just more cases to match):
```python
def handle_expression(e: Expression):
return e.match(
literal=lambda n: ...,
unary_minus=lambda expr: ...,
add=lambda lhs, rhs: ...,
minus=lambda lhs, rhs: ...,
multiply=lambda lhs, rhs: ...,
divide=lambda lhs, rhs: ...)
```
## Compared to Enums
ADTs are somewhat similar to [`Enum`s](https://docs.python.org/3/library/enum.html) from the Python standard library (in fact, the uppercase naming convention is purposely similar).
For example, an `Enum` version of `Expression` might look like:
```python
from enum import Enum, auto
class EnumExpression(Enum):
LITERAL = auto()
UNARY_MINUS = auto()
ADD = auto()
MINUS = auto()
MULTIPLY = auto()
DIVIDE = auto()
```
However, this doesn't allow data to be associated with each of these enum values. A particular value of `Expression` will tell you about a _kind_ of expression that exists, but the operands to the expressions still have to be stored elsewhere.
From this perspective, ADTs are like `Enum`s that can optionally have data associated with each case.
## Compared to inheritance
Algebraic data types are a relatively recent introduction to object-oriented programming languages, for the simple reason that inheritance can replicate the same behavior.
Continuing our examples with the `Expression` ADT, here's how one might represent it with inheritance in Python:
```python
from abc import ABC
class ABCExpression(ABC):
pass
class LiteralExpression(ABCExpression):
def __init__(self, value: float):
pass
class UnaryMinusExpression(ABCExpression):
def __init__(self, inner: ABCExpression):
pass
class AddExpression(ABCExpression):
def __init__(self, lhs: ABCExpression, rhs: ABCExpression):
pass
class MinusExpression(ABCExpression):
def __init__(self, lhs: ABCExpression, rhs: ABCExpression):
pass
class MultiplyExpression(ABCExpression):
def __init__(self, lhs: ABCExpression, rhs: ABCExpression):
pass
class DivideExpression(ABCExpression):
def __init__(self, lhs: ABCExpression, rhs: ABCExpression):
pass
```
This is noticeably more verbose, and the code to consume these types gets much more complex as well:
```python
e: ABCExpression = UnaryMinusExpression(LiteralExpression(3)) # Example of creating an expression
if isinstance(e, LiteralExpression):
result = ... # do something with e.value
elif isinstance(e, UnaryMinusExpression):
result = ... # do something with e.inner
elif isinstance(e, AddExpression):
result = ... # do something with e.lhs and e.rhs
elif isinstance(e, MinusExpression):
result = ... # do something with e.lhs and e.rhs
elif isinstance(e, MultiplyExpression):
result = ... # do something with e.lhs and e.rhs
elif isinstance(e, DivideExpression):
result = ... # do something with e.lhs and e.rhs
else:
raise ValueError(f'Unexpected type of expression: {e}')
```
ADTs offer a simple way to define a type which is _one of a set of possible cases_, and allowing data to be associated with each case and packed/unpacked along with it.
## Examples in other programming languages
Algebraic data types are very common in functional programming languages, like [Haskell](https://www.haskell.org/) or [Scala](https://www.scala-lang.org/), but they're gaining increasing acceptance in "mainstream" programming languages as well.
Here are a few examples.
### [Rust](https://www.rust-lang.org/)
Rust `enum`s are actually full-fledged ADTs. Here's how an `Either` ADT could be defined:
```rust
enum Either {
Left(L),
Right(R),
}
```
### [Swift](https://developer.apple.com/swift/)
[Swift enumerations](https://docs.swift.org/swift-book/LanguageGuide/Enumerations.html) are very similar to Rust's, and behave like algebraic data types through their support of "associated values."
```swift
enum Either {
case Left(L)
case Right(R)
}
```
### [TypeScript](https://en.wikipedia.org/wiki/Microsoft_TypeScript)
[TypeScript](https://en.wikipedia.org/wiki/Microsoft_TypeScript) offers ADTs through a language feature known as ["discriminated unions"](https://www.typescriptlang.org/docs/handbook/advanced-types.html#discriminated-unions).
See this example from their documentation:
```typescript
interface Square {
kind: "square";
size: number;
}
interface Rectangle {
kind: "rectangle";
width: number;
height: number;
}
interface Circle {
kind: "circle";
radius: number;
}
type Shape = Square | Rectangle | Circle;
```
# Installation
To add `adt` as a library in your Python project, simply run `pip` (or `pip3`, as it may be named on your system):
```
pip install algebraic-data-types
```
This will install [the latest version from PyPI](https://pypi.org/project/algebraic-data-types/).
## mypy plugin
The library also comes with a plugin for [mypy](http://mypy-lang.org/) that enables typechecking of `@adt` definitions. **If you are already using mypy, the plugin is required to avoid nonsensical type errors.**
To enable the `adt` typechecking plugin, add the following to a `mypy.ini` file in your project's working directory:
```
[mypy]
plugins = adt.mypy_plugin
```
# Defining an ADT
To begin defining your own data type, import the `@adt` decorator and `Case[…]` annotation:
[//]: # (README_TEST:AT_TOP)
```python
from adt import adt, Case
```
Then, define a new Python class, upon which you apply the `@adt` decorator:
```python
@adt
class MyADT1:
pass
```
For each case (variant) that your ADT will have, declare a field with the `Case` annotation. It's conventional to declare your fields with ALL_UPPERCASE names, but the only true restriction is that they _cannot_ be lowercase.
```python
@adt
class MyADT2:
FIRST_CASE: Case
SECOND_CASE: Case
```
If you want to associate some data with a particular case, list the type of that data in brackets after `Case` (similar to the `Generic[…]` and `Tuple[…]` annotations from `typing`). For example, to add a case with an associated string:
```python
@adt
class MyADT3:
FIRST_CASE: Case
SECOND_CASE: Case
STRING_CASE: Case[str]
```
You can build cases with arbitrarily many associated pieces of data, as long as all the types are listed:
```python
@adt
class MyADT4:
FIRST_CASE: Case
SECOND_CASE: Case
STRING_CASE: Case[str]
LOTS_OF_DATA_CASE: Case[int, str, str, Dict[int, int]]
```
ADTs can also be recursive—i.e., an ADT can itself be stored alongside a specific case—though the class name has to be provided in double quotes (a restriction which also applies to `typing`).
A typical example of a recursive ADT is a linked list. Here, the list is also made generic over a type `T`:
```python
T = TypeVar('T')
@adt
class LinkedList(Generic[T]):
NIL: Case
CONS: Case[T, "LinkedList[T]"]
```
See the library's [tests](tests/) for more examples of complete ADT definitions.
## Generated functionality
Given an ADT defined as follows:
```python
@adt
class MyADT5:
EMPTY: Case
INTEGER: Case[int]
STRING_PAIR: Case[str, str]
```
The `@adt` decorator will automatically generate accessor methods of the following form:
```python
def empty(self) -> None:
return None
def integer(self) -> int:
... # unpacks int value and returns it
def string_pair(self) -> Tuple[str, str]:
... # unpacks strings and returns them in a tuple
```
These accessors can be used to obtain the data associated with the ADT case, but **accessors will throw an exception if the ADT was not constructed with the matching case**. This is a shorthand when you already know the case of an ADT object.
`@adt` will also automatically generate a pattern-matching method, which can be used when you _don't_ know which case you have ahead of time:
[//]: # (README_TEST:IGNORE)
```python
Result = TypeVar('Result')
def match(self,
empty: Callable[[], Result],
integer: Callable[[int], Result],
string_pair: Callable[[str, str], Result]) -> Result:
if ... self was constructed as EMPTY ...:
return empty()
elif ... self was constructed as INTEGER ...:
return integer(self.integer())
elif ... self was constructed as STRING_PAIR ...:
return string_pair(*self.string_pair())
# if pattern match is incomplete, an exception is raised
```
See the library's [tests](tests/) for examples of using these generated methods.
`@adt` will also generate `__repr__`, `__str__`, and `__eq__` methods (only if they are not [defined already](#custom-methods)), to make ADTs convenient to use by default.
## Custom methods
Arbitrary methods can be defined on ADTs by simply including them in the class definition as normal.
For example, to build "safe" versions of the default accessors on `ExampleADT`, which return `None` instead of throwing an exception when the case is incorrect:
```python
@adt
class ExampleADT:
EMPTY: Case
INTEGER: Case[int]
STRING_PAIR: Case[str, str]
@property
def safe_integer(self) -> Optional[int]:
return self.match(empty=lambda: None,
integer=lambda n: n,
string_pair=lambda _a, _b: None)
@property
def safe_string_pair(self) -> Optional[Tuple[str, str]]:
return self.match(empty=lambda: None,
integer=lambda n: None,
string_pair=lambda a, b: (a, b))
```
However, additional fields _must not_ be added to the class, as the decorator will attempt to interpret them as ADT `Case`s (which will fail).