deepuq 0.1.0

a package for investigating and comparing the predictive uncertainties from deep learning models

DeepUQ

DeepUQ is a package for injecting and measuring different types of uncertainty in ML models.

(pypi link if applicable)

(MIT or Apache 2.0 or another requires link changed)

Installation

Install via pypi

To be updated, not yet released.

Clone this repo

First, cd to where you'd like to put this repo and type:

git clone https://github.com/deepskies/DeepUQ.git

Then, cd into the repo:

cd DeepUQ

Install and use poetry to set up the environment

Poetry is our recommended method of handling a package environment as publishing and building is handled by a toml file that handles all possibly conflicting dependencies. Full docs can be found here.

Install instructions:

Add poetry to your python install

pip install poetry

Then, from within the DeepUQ repo, run the following:

Install the pyproject file

poetry install

Begin the environment

poetry shell

Verify DeepUQ is installed correctly

After following the installation instructions, verify installation is functional is all tests are passing by running the following in the root directory:

pytest

How to run the workflow

The scripts can be accessed via the ipython example notebooks or via the model modules (ie DeepEnsemble.py). For example, to ingest data and train a Deep Ensemble from the DeepUQ/ directory:

python src/scripts/DeepEnsemble.py

With no config file specified, this command will pull settings from the default.py file within utils. For the DeepEnsemble.py script, it will automatically select the DefaultsDE dictionary.

Another option is to specify your own config file:

python src/scripts/DeepEnsemble.py --config "path/to/config/myconfig.yaml"

Where you would modify the "path/to/config/myconfig.yaml" to specify where your own yaml lives.

The third option is to input settings on the command line. These choices are then combined with the default settings and output in a temporary yaml.

python src/scripts/DeepEnsemble.py --noise_level "low" --n_models 10 --out_dir ./DeepUQResources/results/ --save_final_checkpoint True --savefig True --n_epochs 10

This command will train a 10 network, 10 epoch ensemble on the low noise data and will save figures and final checkpoints to the specified directory. Required arguments are the noise setting (low/medium/high), the number of ensembles, and the working directory.

For more information on the arguments:

python src/scripts/DeepEnsemble.py --help

The other available script is the DeepEvidentialRegression.py script:

python src/scripts/DeepEvidentialRegression.py --help