semvec/pypi-packages huggingface dataset:
pheval-semphen 0.0.0
Pheval runner for semsimian + phenio db + MONDO (semphen) for disease prioritization via phenotype similarities
Requires Python: <4.0,>=3.9

# pheval_semphen

semsimian + phenio.db for phenopacket disease prioritization<br>
Example of how to run using pheval<br>
 - Download http://data.monarchinitiative.org/monarch-kg/latest/phenio.db.gz<br>
 - Edit config file located in test_configs so that the path_to_phenio references the path to the file we just downloaded
 - pheval run -i test_configs/ -t path/to/phenopackets/ -r "semphenphevalrunner" -o test_output/
<br><br>

Example of how to run just using python executable<br>
 - Download http://data.monarchinitiative.org/monarch-kg/latest/phenio.db.gz<br>
 - python src/pheval_semphen/semphen.py -i path/to/phenopacket(s) -o path/to/results/directory -p path/to/phenio.db file

# Acknowledgements

This [cookiecutter](https://cookiecutter.readthedocs.io/en/stable/README.html) project was developed from the [monarch-project-template](https://github.com/monarch-initiative/monarch-project-template) template and will be kept up-to-date using [cruft](https://cruft.github.io/cruft/).
spuco 2.0.3
SpuCo: Spurious Correlations Datasets and Benchmarks
Requires Python: >=3.10.0

# SpuCo (Spurious Correlations Datasets and Benchmarks)

[![Documentation Status](https://readthedocs.org/projects/spuco/badge/?version=latest)](https://spuco.readthedocs.io/en/latest/?badge=latest)

SpuCo is a Python package developed to further research to address spurious correlations. Spurious correlations arise when machine learning models learn to exploit *easy* features that are not predictive of class membership but are correlated with a given class in the training data. This leads to catastrophically poor performance on the groups of data without such spurious features at test time.

![Diagram illustrating the spurious correlations problem](docs/source/intro_fig.png)

Link to Paper: https://arxiv.org/abs/2306.11957

The SpuCo package is designed to help researchers and practitioners evaluate the robustness of their machine learning algorithms against spurious correlations that may exist in real-world data. SpuCo provides:

- Modular implementations of current state-of-the-art (SOTA) methods to address spurious correlations
- SpuCoMNIST: a controllable synthetic dataset that explores real-world data properties such as spurious feature difficulty, label noise, and feature noise
- SpuCoAnimals: a large-scale vision dataset curated from ImageNet to explore real-world spurious correlations
- SpuCoSun: a large-scale vision dataset with created using backgrounds from SUN397 (class feature) and foregrounds (spurious feature) created using a text-to-image diffusion model corresponding to OpenImagesV7. Two versions of this dataset are provided: SpuCoSun Easy and SpuCoSun Hard with *easy* and *hard* spurious features, respectively. 

> Note: This project is under active development.

# Quickstart

Refer to quickstart for scripts and notebooks to get started with *SpuCo*

You can explore the data with the notebook: [Explore Data](quickstart/explore_data.ipynb)

You can find scripts / notebooks for training with SOTA methods in the folders under quickstart. These are organized by dataset name. 

## Installation

```python
pip install spuco
```

Requires >= Python 3.10

## Using with GuildAI

Creating gpu-affinitized queues 
```
for i in {0..7}; do guild run queue -b --gpus="$i" -y; done
```

## About Us

This package is maintained by [Siddharth Joshi](https://sjoshi804.github.io/) from the BigML group at UCLA, headed by [Professor Baharan Mirzasoleiman](http://web.cs.ucla.edu/~baharan/group.htm).