pychoco

pychoco

Current choco-solver version: 4.10.17

Python bindings for the Choco Constraint programming solver (https://choco-solver.org/).

Choco-solver is an open-source Java library for Constraint Programming (see https://choco-solver.org/). It comes with many features such as various types of variables, various state-of-the-art constraint, various search strategies, etc.

The pychoco library uses a native-build of the original Java Choco-solver library, in the form of a shared library, which means that it can be used without any JVM. This native-build is created with GraalVM native-image tool.

We heavily relied on JGraphT Python bindings source code to understand how such a thing could be achieved, so many thanks to JGraphT authors!

Installation

We automatically build 64-bit wheels for Python versions 3.6, 3.7, 3.8, 3.9, and 3.10 on Linux, Windows and MacOSX. They can be directly downloaded from PyPI (https://pypi.org/project/pychoco/) or using pip:

pip install pychoco

Documentation

If you do not have any knowledge about Constraint Programming (CP) and Choco-solver, you can have a look at https://choco-solver.org/tutos/ for a quick introduction to CP and to Choco-solver features. For this Python API, we also provide an API documentation which is available online at https://pychoco.readthedocs.io/ .

Quickstart

pychoco's API is quite close to Choco's Java API. The first thing to do is to import the library and create a model object:

from pychoco import Model

model = Model("My Choco Model")

Then, you can use this model object to create variables:

intvars = model.intvars(10, 0, 10)
sum_var = model.intvar(0, 100)

You can also create views from this Model object:

b6 = model.int_ge_view(intvars[6], 6)

Create and post (or reify) constraints:

model.all_different(intvars).post()
model.sum(intvars, "=", sum_var).post()
b7 = model.arithm(intvars[7], ">=", 7).reify()

Solve your problem:

model.get_solver().solve()

And retrieve the solution:

print("intvars = {}".format([i.get_value() for i in intvars]))
print("sum = {}".format(sum_var.get_value()))
print("intvar[6] >= 6 ? {}".format(b6.get_value()))
print("intvar[7] >= 7 ? {}".format(b7.get_value()))

> intvars = [3, 5, 9, 6, 7, 2, 0, 1, 4, 8]
> sum = 45
> intvar[6] >= 6 ? False
> intvar[7] >= 7 ? False

Build from source

The following system dependencies are required to build PyChco from sources:

• GraalVM >= 22 (see https://www.graalvm.org/)
• Native Image component for GraalVM (see https://www.graalvm.org/22.1/reference-manual/native-image/)
• Apache Maven (see https://maven.apache.org/)
• Python >= 3.6 (see https://www.python.org/)
• SWIG >= 3 (see https://www.swig.org/)

Once these dependencies are satisfied, clone the current repository:

git clone --recurse-submodules https://github.com/chocoteam/pychoco.git

The --recurse-submodules is necessary as the choco-solver-capi is a separate git project included as a submodule (see https://github.com/chocoteam/choco-solver-capi). It contains all the necessary to compile Choco-solver as a shared native library using GraalVM native-image.

Ensure that the $JAVA_HOME environment variable is pointing to GraalVM, and from the cloned repository execute the following command:

sh build.sh

This command will compile Choco-solver into a shared native library and compile the Python bindings to this native API using SWIG.

Finally, run:

pip install .

And voilà !

Citation

Coming soon.

Getting help or contribute

We do our best to maintain pychoco and keep it up-to-date with choco-solver. However, if you see missing features, if you have any questions about using the library, suggestions for improvements, or if you detect a bug, please open an issue.