semvec/pypi-packages huggingface dataset:
xenusia 0.0.2
A predictor of DNA-binding proteins in archaea

![logo](docs/_static/logo_small.png)

---

## About Xenusia

Xenusia is a Ppredictor of DNA-binding proteins in archaea. 

If you use Xenusia in your research, please consider citing:


## Installation

Package installation should only take a few minutes with any of these methods (pip, source).

### Installing Xenusia with pip:

We suggest to create a local conda environment where to install xenusia. it can be done with:

```sh
conda create -n xenusia
```
and activated with

```sh
conda activate xenusia
```

or

```sh
source activate xenusia
```

We also suggest to install pytorch separately following the instructions from https://pytorch.org/get-started/locally/

```sh
pip install xenusia
```

The procedure will install xenusia in your computer.

### Installing Xenusia from source:

If you want to install Xenusia from this repository, you need to install the dependencies first.
First, install [PyTorch](https://pytorch.org/get-started/locally/) separately following the instructions from https://pytorch.org/get-started/locally/.

Then install the other required libraries:

```sh
pip install numpy scikit-learn requests
```

Finally, you can clone the repository with the following command:

```sh
git clone https://github.com/grogdrinker/xenusia/
```

## Usage

the pip installation will install a script called xenusia_standalone that is directly usable from command line (at least on linux and mac. Most probably on windows as well if you use a conda environemnt).

### Using the standalone
The script can take a fasta file or a sequence as input and provide a prediction as output

```sh
xenusia_standalone AWESAMEPRTEINSEQENCEASINPT
```

or, for multiple sequences, do

```sh
xenusia_standalone fastaFile.fasta
```

To write the output in a file, do

```sh
xenusia_standalone fastaFile.fasta -o outputFilePath
```

### Using Xenusia into a python script

Xenusia can be imported as a python module

```python
from xenusia.run_prediction import predict
proteinSeq1 = "ASDASDASDASDASDASDDDDASD"
proteinSeq2 = "ASDADDDDDDDDDDDDDASDASDDDDASD"
proteinSeq2 = "ASDADFFFFFFFFFDDDDDDDDFFFFFFFFFASD"
inputSequences = {"ID1":proteinSeq1,"ID2":proteinSeq2,"ID3":proteinSeq3}

xenusia_output = predict(inputSequences) # which is a dict containig the predictions

```


## Help

For bug reports, features addition and technical questions please contact gabriele.orlando@kuleuven.be
skill-issue 0.0.2
Seriously, if you have to read this, skill issue on your part
Requires Python: >=3.6

# Skill Issue

So, finally wanna flex those who doubt you as a developer?
well, here it is, destroy them with this library, and the classic "Import skill issue" line. (This is my joke and I am working on copyrighting it idk)

## Functions

- `print_with_user(str)`: Prints the string with "just had a skill issue" appended.

```python
import skill_issue as si 

si.print_with_user("User")

```

The output should look like

```bash
User just had a skill issue
```

- `skillissue()`: Prints "skill issue".

```python
import skill_issue as si 

si.skillissue()

```

The output should look like

```bash
skill issue
```

- `niw()`: Prints "Nah, I'd win".

```python
import skill_issue as si 

si.niw()

```

The output should look like

```bash
Nah, I'd win
```
ragevals 0.3.0
A library for evaluating Retrieval-Augmented Generation (RAG) systems
Requires Python: >=3.10

# RAG Evals

## Overview

RAGEvals is a Python library for evaluating Retrieval-Augmented Generation (RAG) systems. It provides various metrics to evaluate the quality of generated text against reference text.

## Installation

You can install the library using pip:

```bash
pip install ragevals
```

## Usage

Here's how to use the RAGevals library:

```python
from ragevals import RAGevals

# Initialize the evaluator
evaluator = RAGevals()

# Input data
question = "What are the causes of climate change?"
response = "Climate change is caused by human activities."
reference = "Human activities such as burning fossil fuels cause climate change."

# Evaluate the response
metrics = evaluator.evaluate_all(question, response, reference)

# Print the results
print(metrics)

# Get both metric and description
for metric,result in metrics.items():
  print(f"{metric}: {result['score']:.2f} \n{result['description']}")
```



## Metrics

The following metrics are provided by the library:

- **BLEU**: Measures the overlap between the generated output and reference text based on n-grams.
- **ROUGE-1**: Measures the overlap of unigrams between the generated output and reference text.
- **BERT Score**: Evaluates the semantic similarity between the generated output and reference text using BERT embeddings.
- **Perplexity**: Measures how well a language model predicts the text.
- **Diversity**: Measures the uniqueness of bigrams in the generated output.
- **Racial Bias**: Detects the presence of biased language in the generated output.

## Testing

To run the tests, use the following command:

```
python -m unittest discover -s RAGevals -p "test_*.py"
```
## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! If you have any improvements, suggestions, or bug fixes, feel free to create a pull request (PR) or open an issue on GitHub. Please ensure your contributions adhere to the project's coding standards and include appropriate tests.

### How to Contribute

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes.
4. Run tests to ensure everything is working.
5. Commit your changes and push to your fork.
6. Create a pull request (PR) with a detailed description of your changes.

## Contact

If you have any questions or need further assistance, feel free to reach out via [email](mailto:mailmenaveed88@gmail.com).
ioseeth 0.1.22
Utilities to parse blockchain data and look for threat indicators
Requires Python: <4,>=3.8

> :mag_right: parse blockchain data in search of threat indicators.

This project was first meant to enable [Forta network of detection bots][forta-explorer].

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Development](#development)
  - [Changelog](#changelog)
  - [Todo](#todo)
- [Credits](#credits)
- [License](#license)

## Installation

## Usage

## Development

Contributions welcome!

### Changelog

See [CHANGELOG](CHANGELOG.md).

### Todo

See [TODO](TODO.md).

## Credits

The metamorphic indicators were inspired by [the tool from a16z][github-a16z] and [the API by 0age][github-0age].

## License

Licensed under the [aGPL v3](LICENSE).

[forta-explorer]: https://explorer.forta.network/
[github-a16z]: https://github.com/a16z/metamorphic-contract-detector
[github-0age]: https://github.com/0age/metamorphic
tuneable-counterfactuals-explainer 0.0.7
A python package for generating counterfactual explanations for machine learning models
Requires Python: >=3.8

# TuneableCounterfactuals

## Introduction

A general purpose library for generating counterfactuals to explain the outputs of black-box Models


## Installation

```python
pip install -r requirements.txt
```

## Vignette

A simple example that uses our methodology to generate counterfactual explanations for a black box model.

```python
import sklearn

from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

data = fetch_openml('mnist_784', parser='auto')
train_x, test_x, train_y, test_y = train_test_split(
    data.data[data.target.isin(['0', '1'])],
    data.target[data.target.isin(['0', '1'])], 
    test_size=0.2
)

model = MLPClassifier(hidden_layer_sizes=(200,200),alpha=1, max_iter=1000, solver='adam', verbose=10,  random_state=21,tol=0.000000001)
model = make_pipeline(StandardScaler(), model)
model.fit(train_x, train_y)

explainer = Explainer(
    model,
    train_x,
    'target',
    regressor='linear',
    bounding_method='minmax',
    override_variable_bounds=(0, 255)
)

result = explainer.explain(training_data.iloc[id], additional_threshold=0.25)
```

## Contact

Joshan Parmar

Soumya Banerjee (sb2333@cam.ac.uk)

## Citation

Forthcoming


## Graphical abstract

A graphical summary of our work is shown here


![summary](figures/graphical_abstract.png)