AstroML: Machine Learning code for Astronomy
AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, and matplotlib, and distributed under the 3-Clause BSD license. It contains a growing library of statistical and machine learning routines for analyzing astronomical data in python, loaders for several open astronomical datasets, and a large suite of examples of analyzing and visualizing astronomical datasets.
This project was started in 2012 by Jake VanderPlas to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray.
Core and Addons
The project is split into two components. The core
astroML library is
written in python only, and is designed to be very easy to install for
any users, even those who don't have a working C or fortran compiler.
A companion library,
astroML_addons, can be optionally installed for
increased performance on certain algorithms. Every algorithm
astroML_addons has a pure python counterpart in the
astroML implementation, but the
contains faster and more efficient implementations in compiled code.
astroML_addons is installed on your system, the core
astroML library will import and use the faster routines by default.
The reason for this split is the ease of use for newcomers to Python. If the
prerequisites are already installed on your system, the core
library can be installed and used on any system with little trouble. The
astroML_addons library requires a C compiler, but is also designed to be
easy to install for more advanced users. See further discussion in
- HTML documentation: http://astroML.github.com
- Source-code repository: http://github.com/astroML/astroML
- Issue Tracker: http://github.com/astroML/astroML/issues
- Mailing List: https://groups.google.com/forum/#!forum/astroml-general
This package uses distutils, which is the default way of installing python modules. Before installation, make sure your system meets the prerequisites listed in Dependencies, below.
To install the core
astroML package in your home directory, use:
python setup.py install --home
You can specify an arbitrary directory for installation using:
python setup.py install --prefix='/some/path'
To install system-wide on Linux/Unix systems:
python setup.py build sudo python setup.py install
astroML_addons package requires a working C/C++ compiler for
installation. It can be installed using:
python setup_addons.py install
The script can make use of any of the extra options discussed above.
There are three levels of dependencies in astroML. Core dependencies are
required for the core
astroML package. Add-on dependencies are required
for the performance
astroML_addons. Optional dependencies are required
to run some (but not all) of the example scripts. Individual example scripts
will list their optional dependencies at the top of the file.
astroML package requires the following:
- Python version 2.6.x - 2.7.x (astroML does not yet support python 3.x)
- Numpy >= 1.4
- Scipy >= 0.7
- matplotlib >= 0.99
- pyfits >= 3.0. PyFITS is a python reader for Flexible Image Transport System (FITS) files, based on cfitsio. Several of the dataset loaders require pyfits.
This configuration matches the Ubuntu 10.04 LTS release from April 2010.
To run unit tests, you will also need nose >= 0.10
The fast code in
astroML_addons requires a working C/C++ compiler.
Several of the example scripts require specialized or upgraded packages. These requirements are listed at the top of the particular scripts
- scipy version 0.11 added a sparse graph submodule. The minimum spanning tree example requires scipy >= 0.11
- pyMC provides a nice interface for Markov-Chain Monte Carlo. Several examples use pyMC for exploration of high-dimensional spaces. The examples were written with pymc version 2.2
- healpy provides an interface to the HEALPix pixelization scheme, as well as fast spherical harmonic transforms.
This package is designed to be a repository for well-written astronomy code, and submissions of new routines are encouraged. After installing the version-control system git, you can check out the latest sources from github using:
git clone git://github.com/astroML/astroML.git
or if you have write privileges:
git clone firstname.lastname@example.org:astroML/astroML.git
We strongly encourage contributions of useful astronomy-related code: for astroML to be a relevant tool for the python/astronomy community, it will need to grow with the field of research. There are a few guidelines for contribution:
Any contribution should be done through the github pull request system (for
more information, see the
Code submitted to
astroML should conform to a BSD-style license,
and follow the PEP8 style guide.
Documentation and Examples
All submitted code should be documented following the Numpy Documentation Guide. This is a unified documentation style used by many packages in the scipy universe.
In addition, it is highly recommended to create example scripts that show the
usefulness of the method on an astronomical dataset (preferably making use
of the loaders in
astroML.datasets). These example scripts are in the
examples subdirectory of the main source repository.
We made the decision early-on to separate the core routines from high-performance compiled routines. This is to make sure that installation of the core package is as straightforward as possible (i.e. not requiring a C compiler).
Contributions of efficient compiled code to
astroML_addons is encouraged:
the availability of efficient implementations of common algorithms in python
is one of the strongest features of the python universe. The preferred
method of wrapping compiled libraries is to use
cython; other options (weave, SWIG, etc.) are
harder to build and maintain.
Currently, the policy is that any efficient algorithm included in
astroML_addons should have a duplicate python-only implementation in
astroML, with code that selects the faster routine if it's available.
(For an example of how this works, see the definition of the
This policy exists for two reasons:
- it allows novice users to have all the functionality of
astroMLwithout requiring the headache of complicated installation steps.
- it serves a didactic purpose: python-only implementations are often easier to read and understand than equivalent implementations in C or cython.
- it enforces the good coding practice of avoiding premature optimization. First make sure the code works (i.e. write it in simple python). Then create an optimized version in the addons.
If this policy proves especially burdensome in the future, it may be revisited.