bayesian-dpddm 1.0.6

Implements Bayesian D-PDDM for Post-Deployment Deterioration Monitoring of ML models.

Bayesian D-PDDM

Bayesian implementation of the D-PDDM algorithm for post-deployment deterioration monitoring. Bayesian D-PDDM is a Bayesian approximation to the D-PDDM algorithm which provably monitors model deterioration at deployment time. Bayesian D-PDDM:

• Flags deteriorating shifts in the unsupervised deployment data distribution
• Resists flagging non-deteriorating shifts, unlike classical OOD detection leveraging distances and/or metrics between data distributions.

Installation and Requirements

This implementation requires python>=3.11.

The easiest way to install bayesian_dpddm is with pip:

pip install bayesian_dpddm

You can also install by cloning the GitHub repo:

# Clone the repo
git clone https://github.com/teivng/bayesian_dpddm.git

# Navigate into repo directory 
cd bayesian_dpddm

# Install the required dependencies
pip install .

Sweeping Instructions

All experiments are running from the root directory of the repo. We use hydra-core as an argparse on steroids, in tandem with wandb for sweeping. For a sweeping configuration experiments/my_sweep.yaml, run:

wandb sweep experiments/my_sweep.yaml

for which wandb responds with:

wandb: Creating sweep from: experiments/my_sweep.yaml
wandb: Creating sweep with ID: <my_sweep_id>
wandb: View sweep at: https://wandb.ai/<my_wandb_team>/<my_project>/sweeps/<my_sweep_id>

Sweeping locally

Run sweep agent with: wandb agent <my_wandb_team>/<my_project>/<my_sweep_id>.

Sweeping with slurm

sbatch files format pre-configured for the Vaughan cluster. Edit the templates at will.

We execute a script to replace the wandb agent ... line in our .slrm files:

./experiments/replace_wandb_agent.sh "wandb agent <my_wandb_team>/<my_project>/<my_sweep_id>"

Finally, spam jobs on the cluster and maximize your allocation per qos:

./experiments/sbatch_all.sh

Edit this script per your allocation.

Usage and Tutorials

In short, training a DPDDMMonitor consists of a three steps.

1. Train the base model of the monitor on I.I.D. training data
2. With a held-out set of I.I.D. validation data, train the distribution of I.I.D. disagreement rates (Phi) of the monitor
3. Deploy the base model and monitor by periodically running dpddm_test on batches of unsupervised deployment data

When dpddm_test returns True, the monitor recognizes that the base model may severely underperform on the unsupervised deployment data. This is the cue for ML practitioners to inspect the problem further and consider further measures such as adapting and retraining.

For a full tutorial on how to deploy bayesian_dpddm to monitor a downstream task, consider running the guidebook tutorials/classification.ipynb where we train a DPDDMBayesianMonitor to monitor an induced deteriorating shift on the UCI Heart Disease dataset.

Citation

Coming soon.