Triage

ML/Data Science Toolkit for Social Good and Public Policy Problems

Building ML/Data Science systems requires answering many design questions, turning them into modeling choices, which in turn define and machine learning models. Questions such as cohort selection, unit of analysis determination, outcome determination, feature (explanatory variables or predictors) generation, model/classifier training, evaluation, selection, bias audits, interpretation, and list generation are often complicated and hard to make design choices around apriori. In addition, once these choices are made, they have to be combined in different ways throughout the course of a project.

Triage is designed to:

Guide users (data scientists, analysts, researchers) through these design choices by highlighting critical operational use questions.
Provide an integrated interface to components that are needed throughout a ML/data science project workflow.

Getting Started with Triage

Are you completely new to Triage? Run through a quick tutorial hosted on google colab (no setup necessary) to see what triage can do! Tutorial hosted on Google Colab
Dirty Duck Tutorial - Want a more in-depth walk through of triage's functionality and concepts? Go through the dirty duck tutorial that you can install on your local machine with sample data
QuickStart Guide - Try Triage out with your own project and data
Triage Documentation Site - Used Triage before and want more reference documentation?
Development - Contribute to Triage development.

Installation

To install Triage locally, you need:

Ubuntu/RedHat
Python 3.8+
A PostgreSQL 9.6+ database with your source data (events, geographical data, etc) loaded.
- NOTE: If your database is PostgreSQL 11+ you will get some speed improvements. We recommend updating to a recent version of PostgreSQL.
Ample space on an available disk, (or for example in Amazon Web Services's S3), to store the matrices and models that will be created for your experiments

We recommend starting with a new python virtual environment and pip installing triage there.

$ virtualenv triage-env
$ . triage-env/bin/activate
(triage-env) $ pip install triage

If you get an error related to pg_config executable, run the following command (make sure you have sudo access):

(triage-env) $ sudo apt-get install libpq-dev python3.9-dev

Then rerun pip install triage

(triage-env) $ pip install triage

To test if triage was installed correctly, type:

(triage-env) $ triage -h

Data

Triage needs data in a postgres database and a configuration file that has credentials for the database. The Triage CLI defaults database connection information to a file stored in 'database.yaml' (example in example/database.yaml).

If you don't want to install Postgres yourself, try triage db up to create a vanilla Postgres 12 database using docker. For more details on this command, check out Triage Database Provisioner

Configure Triage for your project

Triage is configured with a config.yaml file that has parameters defined for each component. You can see some sample configuration with explanations to see what configuration looks like.

Using Triage

Via CLI:

triage experiment example/config/experiment.yaml

Import as a python package:

from triage.experiments import SingleThreadedExperiment

experiment = SingleThreadedExperiment(
    config=experiment_config, # a dictionary
    db_engine=create_engine(...), # http://docs.sqlalchemy.org/en/latest/core/engines.html
    project_path='/path/to/directory/to/save/data' # could be an S3 path too: 's3://mybucket/myprefix/'
)
experiment.run()

There are a plethora of options available for experiment running, affecting things like parallelization, storage, and more. These options are detailed in the Running an Experiment page.

Development

Triag was initially developed at University of Chicago's Center For Data Science and Public Policy and is now being maintained at Carnegie Mellon University.

To build this package (without installation), its dependencies may alternatively be installed from the terminal using pip:

pip install -r requirement/main.txt

Testing

To add test (and development) dependencies, use test.txt:

pip install -r requirement/test.txt [-r requirement/dev.txt]

Then, to run tests:

pytest

Development Environment

To quickly bootstrap a development environment, having cloned the repository, invoke the executable develop script from your system shell:

./develop

A "wizard" will suggest set-up steps and optionally execute these, for example:

(install) begin

(pyenv) installed

(python-3.9.10) installed

(virtualenv) installed

(activation) installed

(libs) install?
1) yes, install {pip install -r requirement/main.txt -r requirement/test.txt -r requirement/dev.txt}
2) no, ignore
#? 1