Establishing Research Workflows
Overview
Teaching: 15 min
Exercises: 120 minQuestions
What is MAST-ML, and how can it be setup to run on Google Colab?
What is the process of using MAST-ML to execute machine learning workflows?
What advantages does MAST-ML have in developing machine learning models for informatics research?
Objectives
Using the MAST-ML software, students will execute common machine learning workflow steps: data cleaning, feature generation, feature engineering, model assessment through cross validation, model optimization, and model predictions.
Students will compare error metrics from K-fold cross validation using different numbers of folds and assess the effects on reported error metrics.
Background
The MAterial Science Toolkit for Machine Learning (MAST-ML) is a machine learning workflow software package built in python that leverages several other underlying python libraries to enable users to build and test models with little to no python background. The toolkit generates common outputs such as parity plots, learning curves, and standard error metrics which enables new users to easily get to the information needed to make informed decisions about how their models are performing.
Google Colab is a cloud computing resource that allows us to run software on the cloud instead of our local computer, giving access to more computing power and a standardized computing environment. We’re going to use Google Colab to run the the MAST-ML software in a series of tutorials that demonstrate how to perform a basic machine learning workflow.
MAST-ML is divided into a number of sections which allow users to perform ML workflows. These include: Data Importation, Data Cleaning, Feature Generation, Feature Engineering, Data Splitting (Train/Test/Validation), Model Assessment, and Model Optimization By giving the user freedom to choose methods at each of these workflow steps MAST-ML is a extremely customizable in its use compared to some other software tools which are much more structured in the methods used.
For those with more interest in the python programming itself
If you have a little more programming background you may be interested to check out the github page for the code below to see a bit about how it’s structured. There is usually ongoing efforts in the Informatics Skunkworks group at UW-Madison to improve and add features to MAST-ML so if this is something you might be interested in let us know!
For more detailed information on the structure of MAST-ML please see the MAST-ML Documentation.
There are also several other Jupyter Notebooks included there that demonstrate additional functionality in MAST-ML that is not covered in this module.
Activity: Your First MAST ML Run
It’s time to conduct your very first MAST-ML run! For this first run, we will set up a notebook in Google Colab and demonstrate how to call the MAST-ML package to perform machine learning and data science workflow steps.
- Login to Google Colab in your browser. NOTE: Each time you return to Google Colab you will need to rerun the setup instructions as Colab will delete your previous work after signing off.
- Download the MASTML_Colab.ipynb notebook to your local computer. This notebook contains python code to install MAST-ML in the Colab environment and introduces you to the basic ways we can interact with MAST-ML to perform machine learning workflows.
- Upload the MASTML_Colab.ipynb file to Colab using File -> Upload Notebook.
- With the notebook uploaded you should see something like this, from here you should be able to follow along with the instructions in the notebook:
Key Points
MAST-ML is a machine learning workflow package that enables rapid iteration of model building and analysis due to the ability to customize each of the key steps in a machine learning workflow.
With the notebook we’ve provided MAST-ML can be installed on Google Colab
In this activity users have run one example workflow with one set of choices at each step. Users can then change each step to explore other workflow versions and choices.