.. |pipeline| image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/pipeline.svg
:alt: Pipeline status
.. |license| image:: https://img.shields.io/badge/License-New%20BSD-blue.svg
:target: https://opensource.org/licenses/BSD-3-Clause
:alt: License: New BSD
.. |coverage| image:: https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/coverage.svg
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Supervised MultiModal Integration Tool's Readme
===============================================
This project aims to be an easy-to-use solution to run a prior benchmark on a dataset and evaluate mono- & multi-view algorithms capacity to classify it correctly.
Getting Started
---------------
SuMMIT has been designed and uses continuous integration for Linux platforms (ubuntu 18.04), but we try to keep it as compatible as possible with Mac and Windows.
+----------+-------------------+
| Platform | Last positive test|
+==========+===================+
| Linux | |pipeline| |
+----------+-------------------+
| Mac | 1st of May, 2020 |
+----------+-------------------+
| Windows | 1st of May, 2020 |
+----------+-------------------+
Prerequisites
<<<<<<<<<<<<<
To be able to use this project, you'll need :
* `Python 3 <https://docs.python.org/3/>`_
And the following python modules will be automatically installed :
* `numpy <http://www.numpy.org/>`_, `scipy <https://scipy.org/>`_,
* `matplotlib <http://matplotlib.org/>`_ - Used to plot results,
* `sklearn <http://scikit-learn.org/stable/>`_ - Used for the monoview classifiers,
* `joblib <https://pypi.python.org/pypi/joblib>`_ - Used to compute on multiple threads,
* `h5py <https://www.h5py.org>`_ - Used to generate HDF5 datasets on hard drive and use them to spare RAM,
* `pickle <https://docs.python.org/3/library/pickle.html>`_ - Used to store some results,
* `pandas <https://pandas.pydata.org/>`_ - Used to manipulate data efficiently,
* `six <https://pypi.org/project/six/>`_ -
* `m2r <https://pypi.org/project/m2r/>`_ - Used to generate documentation from the readme,
* `docutils <https://pypi.org/project/docutils/>`_ - Used to generate documentation,
* `pyyaml <https://pypi.org/project/PyYAML/>`_ - Used to read the config files,
* `plotly <https://plot.ly/>`_ - Used to generate interactive HTML visuals,
* `tabulate <https://pypi.org/project/tabulate/>`_ - Used to generated the confusion matrix.
* `pyscm-ml <https://pypi.org/project/pyscm-ml/>`_ -
Installing
<<<<<<<<<<
Once you cloned the project from the `github repository <https://github.com/multi-learn/summit/>`_, you just have to use :
.. code:: bash
cd path/to/summit/
pip install -e .
In the `summit` directory to install SuMMIT and its dependencies.
Running the tests
<<<<<<<<<<<<<<<<<
To run the test suite of SuMMIT, run :
.. code:: bash
cd path/to/summit
pip install -e .[dev]
pytest
The coverage report is automatically generated and stored in the ``htmlcov/`` directory
Building the documentation
<<<<<<<<<<<<<<<<<<<<<<<<<<
To locally build the `github-documentation <https://multi-learn.github.io/summit/index.html>`_ run :
.. code:: bash
cd path/to/summit
pip install -e .[doc]
python setup.py build_sphinx
The built html files will be stored in ``path/to/summit/build/sphinx/html``
Running on simulated data
<<<<<<<<<<<<<<<<<<<<<<<<<
For your first go with SuMMIT, you can run it on simulated data with
.. code:: bash
python
>>> from summit.execute import execute
>>> execute("example 1")
This will run the benchmark of `documentation's Example 1 <http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/example1.html>`_.
For more information about the examples, see the `documentation <http://baptiste.bauvin.pages.lis-lab.fr/summit/index.html>`_.
Results will, by default, be stored in the results directory of the installation path :
``path/to/summit/multiview_platform/examples/results``.
The documentation proposes a detailed interpretation of the results and arguments of SuMMIT through `6 tutorials <http://baptiste.bauvin.pages.lis-lab.fr/summit/>`_.
Dataset compatibility
<<<<<<<<<<<<<<<<<<<<<
In order to start a benchmark on your own dataset, you need to format it so SuMMIT can use it. To do so, a `python script <https://gitlab.lis-lab.fr/baptiste.bauvin/summit/-/blob/master/format_dataset.py>`_ is provided.
For more information, see `Example 5 <http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/example5.html>`_
Running on your dataset
+++++++++++++++++++++++
Once you have formatted your dataset, to run SuMMIT on it you need to modify the config file as
.. code:: yaml
name: ["your_file_name"]
pathf: "path/to/your/dataset"
It is however highly recommended to follow the documentation's `tutorials <http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/index.html>`_ to learn the use of each parameter.
Authors
-------
* **Baptiste BAUVIN**
* **Dominique BENIELLI**
* **Alexis PROD'HOMME**
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