Metadata-Version: 2.1
Name: mdlearn
Version: 0.0.1a1
Summary: Machine learning for molecular dynamics
Home-page: https://github.com/ramanathanlab/mdlearn
Author: Alexander Brace, Heng Ma, Debsindhu Bhowmik, Chakra Chennubhotla, Arvind Ramanathan
Author-email: ramanathana@anl.gov
License: MIT License
Download-URL: https://github.com/ramanathanlab/mdlearn/tags
Description: # mdlearn
        
        [![Documentation Status](https://readthedocs.org/projects/mdlearn/badge/?version=latest)](https://mdlearn.readthedocs.io/en/latest/?badge=latest)
        
        mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains [PyTorch](https://pytorch.org/) implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the [kabsch alignment](https://en.wikipedia.org/wiki/Kabsch_algorithm) algorithm and higher-order statistical methods like [quasi-anharmonic analysis](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0015827).
        
        For more details and specific examples of how to use mdlearn, please see our [documentation](https://mdlearn.readthedocs.io/en/latest/).
        
        ## Table of Contents
        1. [Installation](#installation)
        2. [Usage](#usage)
        3. [Contributing](#contributing)
        4. [Acknowledgments](#acknowledgments)
        5. [License](#license)
        
        ## Installation
        
        ### Install latest version with PyPI 
        
        If you have access to an NVIDIA GPU, we highly recommend installing mdlearn into a Conda environment which contains [RAPIDS](https://rapids.ai/) to accelerate t-SNE computations useful for visualizing the model results during training. For the latest [RAPIDS](https://rapids.ai/) version, see [here](https://rapids.ai/start.html#get-rapids). If you don't have GPU support, mdlearn will still work on CPU by using the [scikit-learn](https://scikit-learn.org/stable/) implementation.
        
        Run the following commands with updated versions to create a conda environment:
        ```
        conda create -p conda-env -c rapidsai -c nvidia -c conda-forge cuml=0.19 python=3.7 cudatoolkit=11.2
        conda activate conda-env
        export IBM_POWERAI_LICENSE_ACCEPT=yes
        pip install -U scikit-learn
        ```
        
        Then install mdlearn via: `pip install mdlearn`. 
        
        Some systems require [PyTorch](https://pytorch.org/) to be built from source instead of installed via PyPI or Conda, for this reason we made torch optional dependency. However, it can be installed with mdlearn by running `pip install 'mdlearn[torch]'` for convenience.
        
        
        ### Development
        
        First, follow the above steps to create the conda environment and then install mdlearn with the following commands:
        ```
        git clone https://github.com/ramanathanlab/mdlearn.git
        cd mdlearn
        pip install -r requirements_dev.txt
        pip install -e '.[torch]'
        ```
        
        Then, install pre-commit hooks: this will auto-format and auto-lint _on commit_ to enforce consistent code style:
        
        ```
        pre-commit install
        pre-commit autoupdate
        ```
        
        ## Usage
        
        Train an autoencoder model with only a few lines of code!
        
        ```python
        from mdlearn.nn.models.ae.linear import LinearAETrainer
        
        # Initialize autoencoder model
        trainer = LinearAETrainer(
            input_dim=40, latent_dim=3, neurons=[32, 16, 8], epochs=100
        )
        
        # Train autoencoder on (N, 40) dimensional data
        trainer.fit(X, output_path="./run")
        
        # Generate latent embeddings in inference mode
        z, loss = trainer.predict(X)
        ```
        
        ## Contributing
        
        Please report **bugs**, **enhancement requests**, or **questions** through the [Issue Tracker](https://github.com/ramanathanlab/mdlearn/issues).
        
        If you are looking to contribute, please follow these steps:
        
        1. Fork it!
        2. Create your feature branch: `git checkout -b feature/my-new-feature`
        3. Commit your changes: `git commit -am 'Add some feature'`
        4. Push to the branch: `git push origin feature/my-new-feature`
        5. Submit a pull request :D
        
        
        ## Acknowledgments
        
        - We thank [Matthias Fey](https://github.com/rusty1s) from [*PyTorch Geometric*](https://github.com/rusty1s/pytorch_geometric) for inspiring the design of our neural network base classes and other [PyTorch](https://pytorch.org/) helper functions.
        
        ## License
        
        mdlearn has a MIT license, as seen in the [LICENSE](https://github.com/ramanathanlab/mdlearn/blob/main/LICENSE) file.
        
        
        MIT License
        
        Copyright (c) 2021 Alexander Brace, Arvind Ramanathan
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Keywords: machine-learning,molecular-dynamics,complex-biological-systems
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: torch
