semvec/pypi-packages huggingface dataset:
byma 0.1.11
A Numerical Mathematics Packages with an easier and clean user interface.
Requires Python: >=3.7
Keywords: python, scientific, numerical, bifurcation

# ByMa
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https://pypi.org/project/byma/)
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ByMa is a Python package designed to facilitate numerical mathematics tasks by implementing a range of standard methods, from iterative techniques to bifurcation methods. Noted for its simplicity, clarity, and efficiency, ByMa aims to enhance the learning experience for newcomers to numerical mathematics while streamlining the implementation and utilization of popular scientific libraries such as NumPy, SciPy, and Matplotlib.


## Installation

ByMa is best installed in a [virtual environment](https://docs.python.org/3/library/venv.html).
We state the most common steps for creating and using a virtual environment here.
Refer to the documentation for more details.

To create a virtual environment run
```
python3 -m venv /path/to/new/virtual/environment
```

and to activate the virtual environment, run
```
source /path/to/new/virtual/environment/bin/activate
```

After this, we can install ByMa from the pip package by using
```
pip install byma
```

In case the dependencies are not installed, you can run 
```
pip install -e .
```

## Packages

ByMa consists of several subpackages, each serving a distinct purpose:

- **nuby**: Numerical Bifurcation Analysis Tools
- **iteral**: Tools for iterative algorithms
- **pyplot**: Tools for plotting functions
- **interface**: Interface functionalities

## Authors

* Lorenzo Zambelli [website](https://lorenzozambelli.it)
modelagnosticsafeaipackage 0.9.0
MODEL AGNOSTIC SAFE AI package to measure risks of AI models WITHOUT CONSIDERING TYPE OF THE MODEL

# MODEL AGNOSTIC SAFE AI

This package is based on [safeaipackage](https://pypi.org/project/safeaipackage/). The main difference between these two packages is the input required by the functions. In safeaipackage there are different function which ask for train\test data and a machine learning model. Then, all the process of the training and evaluation is done inside the function. However, in modelagnosticsafeaipackage, it is only needed to provide the actual values (y) and the predicted values by any model you are interested in. Hence, while safeaipackage only works with scikit-learn models, here you can use any kind of model to find the estimated values and then use this package to evaluate risks of your model.


# Install

Simply use:

pip install modelagnosticsafeaipackage


# GitHub

https://github.com/GolnooshBabaei/modelagnosticsafeaipackage


# Example

On GitHub, in the folder "examples", there is a notebook including an example that can help you to understand the functioanlity of this package. 


# Citations

The proposed measures in this package came primarily out of research by 
[Paolo Giudici](https://www.linkedin.com/in/paolo-giudici-60028a/), [Emanuela Raffinetti](https://www.linkedin.com/in/emanuela-raffinetti-a3980215/), 
and [Golnoosh Babaei](https://www.linkedin.com/in/golnoosh-babaei-990077187/) in the [Statistical laboratory](https://sites.google.com/unipv.it/statslab-pavia/home?authuser=0) 
at the University of Pavia. 
This package is based on the following papers. If you use safeaipackage in your research we would appreciate a citation to our papers:
* [Giudici, P., & Raffinetti, E. (2024). RGA: a unified measure of predictive accuracy. Advances in Data Analysis and Classification, 1-27.](https://link.springer.com/article/10.1007/s11634-023-00574-2)
* [Raffinetti, E. (2023). A rank graduation accuracy measure to mitigate artificial intelligence risks. Quality & Quantity, 57(Suppl 2), 131-150.](https://link.springer.com/article/10.1007/s11135-023-01613-y)