supernnova
stable

Getting started

  • System configuration
  • Environment configuration
  • Quickstart guide (GitHub)
  • Quickstart guide (pip)
  • Using the on the fly classification
  • FAQ

Building the database

  • Data walkthrough
  • Data documentation

Experiment Configurations

  • Hyperparameters
  • Experiment Settings

Training models

  • Training walkthrough
  • Training documentation

Validating models

  • Validation walkthrough
  • Validation documentation

Visualizing data and predictions

  • Visualization walkthrough
  • Visualization Documentation

Method paper reproduction

  • Paper reproduction walkthrough
  • Paper reproduction

Notes for Developers

  • Continuos Integration/Continuous Deployment (CI/CD) Workflow
  • Setting-up the Code
  • Poetry and Python environments for development
  • Installing Development Dependencies
  • Guidelines
  • Configuring Services
supernnova
  • Overview: module code

All modules for which code is available

  • supernnova.data.make_dataset
  • supernnova.paper.superNNova_metrics
  • supernnova.paper.superNNova_plots
  • supernnova.paper.superNNova_thread
  • supernnova.training.bayesian_rnn
  • supernnova.training.train_randomforest
  • supernnova.training.train_rnn
  • supernnova.training.vanilla_rnn
  • supernnova.training.variational_rnn
  • supernnova.utils.data_utils
  • supernnova.utils.experiment_settings
  • supernnova.utils.training_utils
  • supernnova.validation.metrics
  • supernnova.validation.validate_randomforest
  • supernnova.validation.validate_rnn
  • supernnova.visualization.early_prediction
  • supernnova.visualization.prediction_distribution
  • supernnova.visualization.visualize

© Copyright 2023, Anais Moller and Thibault de Boissiere. Revision bbbeaa7d.

Built with Sphinx using a theme provided by Read the Docs.