Tools for adaptive parallel sampling of mathematical functions.
adaptive is an open-source Python library designed to
make adaptive parallel function evaluation simple. With
just supply a function with its bounds, and it will be evaluated at the
“best” points in parameter space. With just a few lines of code you can
evaluate functions on a computing cluster, live-plot the data as it
returns, and fine-tune the adaptive sampling algorithm.
WARNING: adaptive is still in a beta development stage
The core concept in
adaptive is that of a learner. A learner
samples a function at the best places in its parameter space to get
maximum “information” about the function. As it evaluates the function
at more and more points in the parameter space, it gets a better idea of
where the best places are to sample next.
Of course, what qualifies as the “best places” will depend on your
adaptive makes some reasonable default choices,
but the details of the adaptive sampling are completely customizable.
The following learners are implemented:
Learner1D, for 1D functions
f: ℝ → ℝ^N,
Learner2D, for 2D functions
f: ℝ^2 → ℝ^N,
LearnerND, for ND functions
f: ℝ^N → ℝ^M,
AverageLearner, For stochastic functions where you want to average the result over many evaluations,
IntegratorLearner, for when you want to intergrate a 1D function
f: ℝ → ℝ,
BalancingLearner, for when you want to run several learners at once, selecting the “best” one each time you get more points.
In addition to the learners,
adaptive also provides primitives for
running the sampling across several cores and even several machines,
with built-in support for
adaptive works with Python 3.6 and higher on Linux, Windows, or Mac,
and provides optional extensions for working with the Jupyter/IPython
The recommended way to install adaptive is using
conda install -c conda-forge adaptive
adaptive is also available on PyPI:
pip install adaptive[notebook]
[notebook] above will also install the optional dependencies for
adaptive inside a Jupyter notebook.
Clone the repository and run
setup.py develop to add a link to the
cloned repo into your Python path:
git clone email@example.com:python-adaptive/adaptive.git cd adaptive python3 setup.py develop
We highly recommend using a Conda environment or a virtualenv to manage
the versions of your installed packages while working on
In order to not pollute the history with the output of the notebooks, please setup the git filter by executing
in the repository.
We implement several other checks in order to maintain a consistent code style. We do this using pre-commit, execute
in the repository.
We would like to give credits to the following people:
- Pedro Gonnet for his implementation of CQUAD, “Algorithm 4” as described in “Increasing the Reliability of Adaptive Quadrature Using Explicit Interpolants”, P. Gonnet, ACM Transactions on Mathematical Software, 37 (3), art. no. 26, 2010.
- Pauli Virtanen for his
AdaptiveTriSamplingscript (no longer available online since SciPy Central went down) which served as inspiration for the ~adaptive.Learner2D.