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توضیحات

Data augmentation of molecules and crystals.

ویژگی	مقدار
سیستم عامل	-
نام فایل	auglichem-0.1.8
نام	auglichem
نسخه کتابخانه	0.1.8
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Yuyang Wange, Rishikesh Magar, Cooper Lorsung, Hariharan Ramasubramanian, Chen Liang, Peiyuan Li, Amir Barati Farimani
ایمیل نویسنده	clorsung@andrew.cmu.edu
آدرس صفحه اصلی	https://github.com/BaratiLab/AugLiChem
آدرس اینترنتی	https://pypi.org/project/auglichem/
مجوز	MIT

# AugLiChem  Welcome to AugLiChem! The augmentation library for chemical systems. This package supports augmentation for both crystaline and molecular systems, as well as provides automatic downloading for our benchmark datasets, and easy to use model implementations. In depth documentation about how to use AugLiChem, make use of transformations, and train models is given on our [website](https://baratilab.github.io/AugLiChem/). ## Installation AugLiChem is a `python3.8+` package. ### Linux It is recommended to use an environment manager such as conda to install AugLiChem. Instructions can be found [here](https://conda.io/projects/conda/en/latest/user-guide/install/index.html). If using conda, creating a new environment is ideal and can be done simply by running the following command: `conda create -n auglichem python=3.8` Then activating the new environment with `conda activate auglichem` AugLiChem is built primarily with `pytorch` and that should be installed independently according to your system specifications. After activating your conda environment, `pytorch` can be installed easily and instructions are found [here](https://pytorch.org/). `torch_geometric` needs to be installed with `conda install pyg -c pyg -c conda-forge`. Once you have `pytorch` and `torch_geometric` installed, installing AugLiChem can be done using PyPI: `pip install auglichem` ### MacOS ARM64 Architecture A more involved install is required to run on the new M1 chips since some of the packages do not have official support yet. We are working on a more elegant solution given the current limitations. First, download this repo. If you do not have it yet,, conda for ARM64 architecture needs to be installed. This can be done with Miniforge (which contains conda installer) which is installed by following the guide [here](https://github.com/conda-forge/miniforge) Once you have miniforge compatible with ARM64 architecture, a new environment with rdkit can be i nstalled. If you do not specify `python=3.8` it will default to `python=3.9.6` as of the time of writing th is. `conda create -n auglichem python=3.8 rdkit` Now activate the environment: `conda activate auglichem` From here, individual packages can be installed: `conda install -c pytorch pytorch` `conda install -c fastchan torchvision` `conda install scipy` `conda install cython` `conda install scikit-learn` `pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cpu.html` `pip install torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+cpu.html` `pip install torch-geometric` Before installing the package, you must go into `setup.py` in the main directory and comment out `rdkit-pypi` and `tensorboard` from the `install_requires` list since they are already installed. Not commenting these packages out will result in an error during installation. Finally, run: `pip install .` Usage guides are provided in the `examples/` directory and provide useful guides for using both the molecular and crystal sides of the package. Make sure to install `jupyter` before working with examples, using `conda install jupyter`. After installing the package as described above, the example notebooks can be downloaded separately and run locally. ## Authors Rishikesh Magar\*, Yuyang Wang\*, Cooper Lorsung\*, Hariharan Ramasubramanian, Chen Liang, Peiyuan Li, Amir Barati Farimani \*Equal contribution  ## Paper Our paper can be found [here](https://arxiv.org/abs/2111.15112) ## Citation If you use AugLiChem in your work, please cite: ``` @misc{magar2021auglichem, title={AugLiChem: Data Augmentation Library ofChemical Structures for Machine Learning}, author={Rishikesh Magar and Yuyang Wang and Cooper Lorsung and Chen Liang and Hariharan Ramasubramanian and Peiyuan Li and Amir Barati Farimani}, year={2021}, eprint={2111.15112}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ## License AugLiChem is MIT licensed, as found in the [LICENSE](https://github.com/BaratiLab/AugLiChem/blob/main/LICENSE) file. Please note that some of the dependencies AugLiChem uses may be licensed under different terms.

نیازمندی

مقدار	نام
>=1.21.1	numpy
>=6.0.1	pytest
-	pytest-cov
-	sklearn
>=1.3.1	pandas
>=3.4.2	matplotlib
>=5.4.1	pyyaml
-	tqdm
>=3.22.0	ase
>=2022.0.11	pymatgen
-	rdflib
>=1.10	torch
-	rdkit-pypi
>=2.4.1	tensorboard

زبان مورد نیاز

مقدار	نام
>=3.8	Python

نحوه نصب

نصب پکیج whl auglichem-0.1.8:

pip install auglichem-0.1.8.whl

نصب پکیج tar.gz auglichem-0.1.8:

pip install auglichem-0.1.8.tar.gz