Software

  • Chemfeat



    • The project provides a Python package and command-line tool for generating feature vectors from molecules. The feature sets to include in the feature vectors are configurable via a simple YAML configuration file. Molecules are specified as lists of Inchis.



    • https://gitlab.inria.fr/jrye/chemfeat
  • Hydronaut



    • A Python framework for machine- and deep-learning that makes it easy to use Hydra for hyperparameter configuration and MLflow for experiment tracking and result distribution. The user only needs to create a single YAML configuration file and a subclass of Hydronaut.Experiment to use the framework.

      Hydra allows the user to systematically sweep all hyperparameter combinations or optimize them use different strategies with plugins for libraries such as Optuna.

      MLflow provides a web interface, command-line interface and Python API for exploring and sharing the results.

      The framework is fully compatible with PyTorch Lightning and provides a custom subclass to facilitate its use.




    • https://gitlab.inria.fr/jrye/hydronaut
  • jnp



    • A Python library and command-line tool to automatically export presentations from Jupyter notebooks along with custom CSS and other user-configurable website content.



    • https://gitlab.inria.fr/jrye/jnp
  • MLflow Extra



    • Utility scripts and matching python module for working with MLflow directories. It mainly provides functionality for moving around and regrouping "mlruns" directories which is useful in contexts such as migrating results from a cluster.



    • https://gitlab.inria.fr/jrye/mlflow-extra
  • MolPred



    • A Hydronaut-based framework that uses ChemFeat to generate feature vectors for training machine- and deep-learning models to predict properties of molecules.



    • https://gitlab.inria.fr/jrye/molpred
  • PyPTU



    • Parse PicoQuant PTU files in Python. It includes a Python package and a command-line utility.



    • https://gitlab.inria.fr/jrye/pyptu
  • SWoTTeD



    • SWoTTeD is a tensor decomposition framework to extract temporal phenotypes from structured data. Most recent decomposition models allow extracting phenotypes that only describe snapshots of typical profiles, also called daily phenotypes. However, SWoTTeD extends the notion of daily phenotype into temporal phenotype describing an arrangement of features over a time window.



    • https://hsebia.gitlabpages.inria.fr/swotted/readme.html

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