mFlow Overview

mFlow is a Python module for specifying and executing machine learning experimentation workflows. It organizes both data transformations and common experimental procedures into workflow blocks. mFlow focuses on the multi-core parallel setting and interoperates with machine learning model implementations that follow the scikit-learn model class structure. mFlow can also interoperate with Apache Spark and the MD2K Cerebral Cortex library to split workflow execution across a distributed pre-processing and feature extraction phase followed by a multi-core parallel model estimation phase. mFlow uses Pandas dataframes as the primary data structure for representing data sets and results.

mFlow supports a range of experimental workflows with a focus on multi-level data where individual data cases are nested within groups. It supports partitioning data sets both at the instance level and at the group level for most workflows. Experimental designs currently supported by mFlow include:

• Train/Test performance assessment with instance-level partitioning

• Train/test performance assessment with group-level partitioning

• Cross-validation performance assessment with instance-level partitioning

• Cross-validation performance assessment with group-level partitioning

• Within group Train/Test performance assessment with random partitioning within groups

• Within group Train/Test performance assessment with sequential partitioning within groups

mFlow also includes a library of example workflows illustrating the application of data transformations and experimental workflows on open data sets in the mobile health and activity recognition domains.

Installation

mFlow requires Python 3.6 or higher. To install mFlow using pip, clone this repository and run pip install:

git clone https://github.com/mlds-lab/mFlow.git
pip3 install ./mFlow

Examples

See the Examples directory for a list of mFlow examples that can be run locally or launched in Google Colab.

Contents:

• mFlow.Blocks Package
  • mFlow.Blocks.ccwrapper Module
  • mFlow.Blocks.data_loader_extrasensory Module
  • mFlow.Blocks.data_loader_wesad Module
  • mFlow.Blocks.experimental_protocol Module
  • mFlow.Blocks.filter Module
  • mFlow.Blocks.imputer Module
  • mFlow.Blocks.normalizer Module
  • mFlow.Blocks.results_analysis Module
• mFlow.Utilities Package
  • mFlow.Utilities.utilities Module
• mFlow.Workflow Package
  • mFlow.Workflow.compute_graph Module
  • mFlow.Workflow.scheduler Module
  • mFlow.Workflow.workflow Module

Contributors

Link to the list of contributors who participated in this project.

License

This project is licensed under the BSD 2-Clause - see the license file for details.

Acknowledgments

• National Institutes of Health - Big Data to Knowledge Initiative Grant: 1U54EB020404

• National Science Foundation Grant: 1823283

Indices and tables

• Index

• Module Index

• Search Page