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Exhaustively enumerate a combinatorial space represented by a function

Project description

exhaust

A Python library to exhaustively enumerate a combinatorial space represented by a function.

The API is modelled after Python's random module and should feel familiar. An additional convenience function maybe() was added to cover a common case.

If you're missing a function and the corresponding space can be enumerated feel free to open an issue. Any functions that generate real-valued distributions cannot be supported.

Example

import exhaust

def generate_character(state: exhaust.State):
    eyes = []
    for _ in range(state.randint(1, 3)):
        eyes.append({
            'color': state.choice(['brown', 'blue']),
            'glowing': state.maybe()
        })
    size = 'giant' if len(eyes) == 1 else 'normal'
    accessories = []
    if len(eyes) == 2:
        if state.maybe():
            accessories.append('hat')
        if state.maybe():
            accessories.append('ring')    
    character = {
        'size': size,
        'eyes': eyes,
        'accessories': accessories
    }
    return character

# iterates over a space of 132 characters
for character in exhaust.space(generate_character):
    print(character)

As you can see, navigating the space works fine within loops as well. Each time a function from the State object is called (like maybe()), you can think of it as forking the current path into multiple branches, leading to a tree that gets explored. While exploring, the user-defined function is called for each path of the tree.

See the examples/ folder for further examples that can be run on the command line.

See the API Documentation for all available State functions.

Installation

pip install exhaust

Development

Requires pip >= 21.3

Editable install:

pip install -e .

Run tests:

pip install -r requirements-test.txt
pytest

Build docs:

pip install -r requirements-docs.txt
sphinx-build -b html docs dist-docs

Build wheel:

pip install build
python -m build --wheel

Acknowledgments

This package is inspired by KerasTuner's method of defining hyperparameter search spaces.

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