Client for the Polaris Hub.
Project description
Polaris establishes a novel, industry‑certified standard to foster the development of impactful methods in AI-based drug discovery.
This library is a Python client to interact with the Polaris Hub. It allows you to:
- Download Polaris datasets and benchmarks.
- Evaluate a custom method against a Polaris benchmark.
- Create and upload new datasets and benchmarks.
Quick API Tour
import polaris as po
# Load the benchmark from the Hub
benchmark = po.load_benchmark("polaris/hello-world-benchmark")
# Get the train and test data-loaders
train, test = benchmark.get_train_test_split()
# Use the training data to train your model
# Get the input as an array with 'train.inputs' and 'train.targets'
# Or simply iterate over the train object.
for x, y in train:
...
# Work your magic to accurately predict the test set
predictions = [0.0 for x in test]
# Evaluate your predictions
results = benchmark.evaluate(predictions)
# Submit your results
results.upload_to_hub(owner="dummy-user")
Documentation
Please refer to the documentation, which contains tutorials for getting started with polaris and detailed descriptions of the functions provided.
Installation
You can install polaris using conda/mamba/micromamba:
conda install -c conda-forge polaris
You can also use pip:
pip install polaris-lib
Development lifecycle
Setup dev environment
conda env create -n polaris -f env.yml
conda activate polaris
pip install --no-deps -e .
Other installation options
Alternatively, using [uv](https://github.com/astral-sh/uv):
```shell
uv venv -p 3.12 polaris
source .venv/polaris/bin/activate
uv pip compile pyproject.toml -o requirements.txt --all-extras
uv pip install -r requirements.txt
```
Tests
You can run tests locally with:
pytest
License
Under the Apache-2.0 license. See LICENSE.
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