Machine learning tools for the Geoscience Australia uncover project
Project description
Machine learning tools for the Geoscience Australia uncover project.
Quickstart
Before you start, make sure your system has the following packages installed,
gdal (libgdal-dev)
openmpi
hdf5
We strongly recommend using a virtual environment. To install, simply run setup.py:
$ python setup.py install
or install with pip:
$ pip install git+https://github.com/GeoscienceAustralia/uncover-ml.git@release
The python requirements should automatically be built and installed.
Cubist
In order to use the cubist regressor, you need to first make sure cubist is installed. This is easy with our simple installation script, invoke it with:
$ ./makecubist <installation-path>
Once cubist is installed, it will add a configuration file to the script, if you like, you can test that it’s been installed in the correct place by checking the contents of uncover-ml/cubist_config.py, its presence indicates that the installation completed successfully.
Next you need to rerun the setup script with:
$ python setup.py install
Which will ensure the cubist_config has been added successfully. Now you should be able to use the cubist regressor in the pipeline file.
Running
See the usage documentation.
Running on NCI
Please see The PBS Readme .
Collaboration
This software is jointly developed by NICTA and Geoscience Australia. For a list of features still to be implemented, see the issue tracker.
Useful Links
- Home Page
- Documentation
- Issue tracking
Bugs & Feedback
For bugs, questions and discussions, please use Github Issues.
Documentation
The full documentation is at http://GeoscienceAustralia.github.io/uncover-ml/.
History
0.1.0 (2016-05-01)
First release on PyPI.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for uncover_ml-0.1.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7634c4253cc8d9b251d67940a7ef66491a1b5a129407d413e1828dbe7e5ae3e6 |
|
MD5 | 9398fac743552751cee374dbb3e72230 |
|
BLAKE2b-256 | 4312c596d4d55b51c686b0483242fce3244bf9c0a58c33d80f65fa0053848ccc |