daml 0.56.0
Data-Analysis Metrics Library (DAML)
About DAML
The Data-Analysis Metrics Library, or DAML, focuses on characterizing image data and its impact on model performance across classification and object-detection tasks.
Model-agnostic metrics that bound real-world performance
relevance/completeness/coverage
metafeatures (data complexity)
Model-specific metrics that guide model selection and training
dataset sufficiency
data/model complexity mismatch
Metrics for post-deployment monitoring of data with bounds on model performance to guide retraining
dataset-shift metrics
model performance bounds under covariate shift
guidance on sampling to assess model error and model retraining
Getting Started
Requirements
Python 3.9-3.11
Installing DAML
You can install DAML directly from pypi.org using the following command. The optional dependencies of DAML are torch, tensorflow and all. Using torch enables Sufficiency metrics, and tensorflow enables OOD Detection.
pip install daml[all]
Installing DAML from GitHub
To install DAML from source locally on Ubuntu, you will need git-lfs to download larger, binary source files and poetry for project dependency management.
sudo apt-get install git-lfs
pip install poetry
Pull the source down and change to the DAML project directory.
git clone https://github.com/aria-ml/daml.git
cd daml
Install DAML with optional dependencies for development.
poetry install --all-extras --with dev
Now that DAML is installed, you can run commands in the poetry virtual environment by prefixing shell commands with poetry run, or activate the virtual environment directly in the shell.
poetry shell
Documentation and Tutorials
For more ideas on getting started using DAML in your workflow, additional information and tutorials are in our Sphinx documentation hosted on Read the Docs.
Attribution
This project uses code from the Alibi-Detect python library developed by SeldonIO. Additional documentation from the developers are also available here.
POCs
POC: Scott Swan @scott.swan
DPOC: Andrew Weng @aweng
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
There are no reviews.