.. CISS-VAE documentation master file, created by sphinx-quickstart on Mon Jul 21 09:06:45 2025. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. CISS-VAE documentation ====================== The Clustering-Informed Shared-Structure Variational Autoencoder (CISS-VAE) is a flexible deep learning model for missing data imputation that is particularly well-suited to MNAR (Missing Not at Random) scenarios where missingness patterns are informative. It also functions effectively under MAR (Missing at Random) assumptions. Please see our `publication `_ for more details. The model uses unsupervised clustering to capture distinct patterns of missingness and leverages a mix of shared and unshared encoder and decoder layers, allowing knowledge transfer across clusters and enhancing parameter stability. Its iterative learning procedure improves imputation accuracy compared to traditional training approaches. .. image:: CISSVAEModelDiagram.png The CISS-VAE package also offers the :py:func:`ciss_vae.training.autotune.autotune` function, which can help select the best hyperparameters for your model within a user-defined search space. The autotune function has compatibility with Optuna Dashboard for viewing hyperparameter importance trends. The R package associated with this model can be found at `rCISS-VAE `_. .. toctree:: :maxdepth: 1 :caption: Contents: quickstart.ipynb vignette.ipynb binary_vignette.ipynb missingness_prop_vignette.ipynb dni_vignette.ipynb api Installation ------------ To install via PyPI: .. code-block:: bash pip install ciss-vae To install via github: .. code-block:: bash pip install git+https://github.com/CISS-VAE/CISS-VAE-python.git The github repo can be found `here `_. Features -------- - Cluster-specific VAE architecture - Compatible with real-world missing data (MAR, MNAR) - Optuna-based hyperparameter tuning Need help? See the `vignette `_ or the full `API reference `_.