Skip to content
Snippets Groups Projects
Select Git revision
  • b62a26ac20ffe86ba1836a1cea502d46fa83bd1a
  • master default protected
2 results

ipi_extract.py

Blame
    • example1.rst 15.79 KiB

    First steps with Multiview platform

    Context

    This platform aims at running multiple state-of-the-art classifiers on a multiview dataset in a classification context. It has been developed in order to get a baseline on common algorithms for any classification task.

    Adding a new classifier (monoview and/or multiview) as been made as simple as possible in order for users to be able to customize the set of classifiers and test their performances in a controlled environment.

    Introduction to this tutorial

    This tutorial will show you how to use the platform on simulated data, for the simplest problem : biclass classification.

    The data is naively generated TODO : Keep the same generator ?

    Getting started

    Importing the platform's execution function

    >>> from multiview_platform.execute import execute

    Understanding the config file

    The config file that will be used in this example is located in multiview-machine-learning-omis/multiview_platform/examples/config_files/config_exmaple_1.yml We will decrypt the main arguments :

    The first part of the arguments are the basic ones : - log: True allows to print the log in the terminal, - name: ["plausible"] uses the plausible simulated dataset, - random_state: 42 fixes the random state of this benchmark, it is useful for reproductibility, - full: True the benchmark will used the full dataset, - res_dir: "examples/results/example_1/" the results will be saved in multiview-machine-learning-omis/multiview_platform/examples/results/example_1

    Then the classification-related arguments - split: 0.8 means that 80% of the dataset will be used to test the different classifiers and 20% to train them - type: ["monoview", "multiview"] allows for monoview and multiview algorithms to be used in the benchmark - algos_monoview: ["all"] runs on all the available monoview algorithms (same for algos_muliview) - metrics: ["accuracy_score", "f1_score"] means that the benchmark will evaluate the performance of each algortihms on these two metrics.

    Then, the two following categories are algorithm-related and contain all the default values for the hyper-parameters.

    Start the benchmark

    During the whole benchmark, the log file will be printed in the terminal. To start the benchmark run :