Metadata-Version: 2.1
Name: stVAE
Version: 0.2.10
Summary: Style transfer variational autoencoder
Home-page: https://github.com/NRshka/stvae/source
Author: ['N. Russkikh', 'A. Makarov', 'D. Antonets', 'D. Shtokalo', 'Y. Vyatkin']
Author-email: makarov.alxr@yandex.ru
License: MIT license
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Natural Language :: Russian
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Requires-Dist: dataclasses (==0.7)
Requires-Dist: torch
Requires-Dist: pytorch-ignite (==0.3.0)
Requires-Dist: numpy
Requires-Dist: pandas (==1.1.1)
Requires-Dist: typing
Requires-Dist: tensorboardX (==2.0)
Requires-Dist: scikit-learn (==0.23.2)
Requires-Dist: scipy (==1.4.1)
Requires-Dist: scvi
Requires-Dist: scanpy
Requires-Dist: torch-optimizer (==0.0.1a11)
Requires-Dist: scgen
Requires-Dist: requests

==================
Style transfer VAE
==================

.. image:: https://img.shields.io/pypi/v/stvae?color=green
    :target: https://pypi.org/project/stVAE/

.. image:: https://travis-ci.org/NRshka/stvae.svg?branch=master
    :target: https://travis-ci.org/NRshka/stvae

The official pytorch implementation of "*Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis*".
The package contains a code for training and testing the model, as well as a code for working with different types of datasets.

**Installation**

To install the latest version from PyPI, use:

>>> pip install stvae

**Benchmarks**

The original code containing code with testing several models can be found here_.

.. _here: https://github.com/NRshka/stvae-source

**Example**

.. code-block:: python

   ds = stvae.datasets.MouseDataset(download=True) # download data to the current directory
   cfg = stvae.Config()
   train, test, classif = ds.split(0.15, True, 0.15)
   cfg.count_classes = ds.n_labels
   cfg.count_classes = ds.n_batches
   cfg.input_dim = ds.nb_genes
   cfg.use_cuda = True # if you have a CUDA compatibility gpu
   cfg.epochs = 600 # number of training epocs
   cfg.classifier_epochs = 450 # number of epochs for testing classifirs training
   model = stvae.stVAE(cfg)
   model.train(train, None)
   d = model.test(test, classif)

