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https://github.com/kjappelbaum/oximachinerunner
An easy API for using oximachine.
https://github.com/kjappelbaum/oximachinerunner
Last synced: 2 months ago
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An easy API for using oximachine.
- Host: GitHub
- URL: https://github.com/kjappelbaum/oximachinerunner
- Owner: kjappelbaum
- License: mit
- Created: 2020-05-22T16:41:23.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-08T00:40:23.000Z (3 months ago)
- Last Synced: 2024-11-08T04:10:34.864Z (2 months ago)
- Language: Python
- Homepage:
- Size: 40.4 MB
- Stars: 7
- Watchers: 3
- Forks: 5
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# oximachinerunner
[](https://github.com/kjappelbaum/oximachinerunner/actions)
[](https://mybinder.org/v2/gh/kjappelbaum/oximachinerunner/master?filepath=examples%2Fexample.ipynb)
[](https://oximachinerunner.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/kjappelbaum/oximachinerunner)
## Installation
On macOS you need to run `brew install libomp` first to enable multithreading for the `XGBoost` library.
Ideally, you install everything in a clean environment, e.g., using conda
```bash
conda create -n test_oximachine_runner python=3.7 -y
```
Then activate with `conda activate test_oximachine_runner`.
### Latest stable release
```bash
pip install oximachinerunner
```
Note that the installation requires significant (>500 MB) storage space since the ensembles use k-nearest neighbors models.
### Development version
```bash
pip install git+https://github.com/kjappelbaum/oximachinerunner.git
```
## Usage
#### Loading the model
```python
from oximachinerunner import OximachineRunner
runner = OximachineRunner()
```
The `OximachineRunner` can be initialized with a modelname from `runner.available_models`.
By default, models will be automatically downloaded (upon first use) if there are not yet in the correct folder:
```
/Users/kevinmaikjablonka/opt/miniconda3/envs/test_oximachine_runner/lib/python3.7/site-packages/oximachinerunner/assets/all_202000830/classifier.joblib are not exist or md5 is wrong.
Download file from https://www.dropbox.com/s/lc2z4abaycjbbe1/classifier.joblib?dl=1
2.9% of 527.44M
```
To disable this behavior of, set `OximachineRunner(automatic_download=False)` and manually download your model, e.g. using a function from the `utils` module.
#### Predicting oxidation states
The `run_oximachine` function accepts `pymatgen.Structure`, `ase.Atoms` and `str` as well as `os.PathLike`.
The latter two are expected to be paths to a file that is then parsed with `pymatgen`.
```python
runner.run_oximachine('oximachinerunner/assets/ACODAA.cif')
```
The function prints for how many sites it will run the model.
```
featurize.py: iterating over 6 metal sites
```
It returns an `OrderedDict` with the fields:
- `metal_indices`: A list of indices of the metal sites
- `metal_symbols`: A list of symbols of the metal atoms
- `prediction`: A list of oxidation state predictions
- `max_probabs`: For each metal site the maximum confidence of all 4 models.
- `base_predictions`: For each metal site a list of the oxidation state predictions for each of the 4 models.
### Development setup
```
git clone https://github.com/kjappelbaum/oximachinerunner
pip install -e .[dev]
```
## Reference
Jablonka, Kevin Maik; Ongari, Daniele; Moosavi, Seyed Mohamad; Smit, Berend (2020): Using Collective Knowledge to Assign Oxidation States. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.11604129.v1