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 :

• Python 3.6

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.

They are all tested in multiview-machine-mearning-omis/multiview_platform/MonoMutliViewClassifiers/Versions.py which is automatically checked each time you run the execute script

Installing

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

pip install -e .

In the multiview_machine-learning-omis directory.

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 documentations 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 :

from multiview_platform.execute import execute
execute(config_path="/absolute/path/to/your/config/file")

For further information about classifier-specific arguments, see the documentation.

Dataset compatibility

In order to start a benchmark on your dataset, you need to format it so the script can use it. You can have either a directory containing .csv files or a HDF5 file.

If you have multiple .csv files, you must organize them as :

• top_directory/database_name-labels.csv
• top_directory/database_name-labels-names.csv
• top_directory/Views/view_name.csv or top_directory/Views/view_name-s.csv if the view is sparse

With top_directory being the last directory in the pathF argument

If you already have an HDF5 dataset file it must be formatted as :

One dataset for each view called ViewX with X being the view index with 2 attribures :

• attrs["name"] a string for the name of the view
• attrs["sparse"] a boolean specifying whether the view is sparse or not
• attrs["ranges"] a np.array containing the ranges of each attribute in the view (for ex. : for a pixel the range will be 255, for a real attribute in [-1,1], the range will be 2).
• attrs["limits"] a np.array containing all the limits of the attributes int he view. (for ex. : for a pixel the limits will be [0, 255], for a real attribute in [-1,1], the limits will be [-1,1]).

One dataset for the labels called Labels with one attribute :

• attrs["names"] a list of strings encoded in utf-8 namig the labels in the right order

One group for the additional data called Metadata containing at least 3 attributes :

• attrs["nbView"] an int counting the total number of views in the dataset
• attrs["nbClass"] an int counting the total number of different labels in the dataset
• attrs["datasetLength"] an int counting the total number of examples in the dataset

Running on your dataset

In order to run the script on your dataset you need to use :

cd multiview-machine-learning-omis/multiview_platform
python execute.py -log --name <your_dataset_name> --type <.cvs_or_.hdf5> --pathF <path_to_your_dataset>

This will run a full benchmark on your dataset using all available views and labels.

You may configure the --CL_statsiter, --CL_split, --CL_nbFolds, --CL_GS_iter arguments to start a meaningful benchmark

Running the tests

/!\ still in development, test sucess is not meaningful ATM /!\

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

cd multiview-machine-learning-omis/
python -m unittest discover

Author

• Baptiste BAUVIN

Contributors

• Mazid Osseni
• Alexandre Drouin
• Nikolas Huelsmann