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README.md

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  • README.md 5.64 KiB

    License: GPL v3 Build Status

    Mono- and Multi-view classification benchmark

    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

    Prerequisites

    To be able to use this project, you'll need :

    And the following python modules :

    • numpy, scipy,
    • matplotlib - Used to plot results,
    • sklearn - Used for the monoview classifiers,
    • joblib - Used to compute on multiple threads,
    • h5py - Used to generate HDF5 datasets on hard drive and use them to spare RAM,
    • pickle - Used to store some results,
    • pandas - Used to manipulate data efficiently,
    • six -
    • m2r - Used to generate documentation from the readme,
    • docutils - Used to generate documentation,
    • pyyaml - Used to read the config files,
    • plotly - Used to generate interactive HTML visuals,
    • tabulate - Used to generated the confusion matrix.

    Installing

    Once you cloned the project from the gitlab repository, you just have to use :

    pip install -e .

    In the multiview_machine-learning-omis directory to install SuMMIT and its dependencies.

    Running on simulated data

    In order to run it you'll need to try on simulated data with the command

    from multiview_platform.execute import execute
    execute()

    This will run the first example. For more information about the examples, see the documentation Results will be stored in the results directory of the installation path : path/to/install/multiview-machine-learning-omis/multiview_platform/examples/results. The documentation proposes a detailed interpretation of the results.

    Discovering the arguments

    All the arguments of the platform are stored in a YAML config file. Some config files are given as examples. The file stored in multiview-machine-learning-omis/config_files/config.yml is documented and it is highly recommended to read it carefully before playing around with the parameters.

    You can create your own configuration file. In order to run the platform with it, run :