semvec/pypi-packages huggingface dataset:
Python-Multi-Helper 1.0.0
Several common helpers for Python coding

# PyMultiHelper

This library intends to serve as a multi-purpose Python Helpers collection

## Installation

Use the 'pip' package manager to install this package.

```bash
pip install PyMultiHelper
```

## Usage

Each sub-library has a collection of Methods, each one with it's own PyDoc explaining the usage. 

## Contributions

Pull requests are welcome, even more if poiting toward bug corrections.

## Publishing

This project is published on:
- GitHub: https://github.com/anderbytes/PyMultiHelper
- PyPi: https://pypi.org/project/PyMultiHelper/

## Licença

[MIT](https://mit-license.org/)
psruq-python 0.0.4
Code for uncertainty quantification with proper scoring rules.
Requires Python: >=3.10

# Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules

This repository contains code for the paper "Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules," submitted to NeurIPS 2024.

### UV
We use uv to manage dependecies. You can find an installation guide and documentation [here](https://docs.astral.sh/uv/getting-started/installation/#standalone-installer).

**Install uv**

For Linux/MacOS the following line will install uv on your system:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

**Install dependencies**

**All the commands should be executed from the folder with pyproject.toml file**

To install dependencies from pyproject.toml file using
```bash
uv sync
```
It will create a .venv folder, that will contain a virtual environment and all the needed dependencies.

**Run python script**

uv installs dependencies in special virutal env, that is located in .venv/. To run any python code snippet with it you should run:
```bash
uv run python ...
```
e.g.
```bash
cd experiments/laplace
uv run python main.py -f checkpoints/resnet18_ce.pth -d cifar10_one_batch -v
```

### Repository Structure

The repository is organized as follows:

### Experiments
This folder contains different experiments related to the paper.
- cifar10: Contains training scripts for cifar10 dataset.
- laplace: Contains inference scripts for Laplace Redux – Effortless Bayesian Deep Learning model.

### Embedding Extraction
- source/source/evaluation_utils.py: Script to extract embeddings after the ensembles are trained.

### Notebooks
Folder notebooks/ have notebooks with data analysis of experiments.
- joint_tables.ipynb: Prepares comprehensive pandas tables for both problems.
- ood_analysis.ipynb: Generates plots for the OOD problem.
- ood_visualizations.ipynb: Creates visualizations for the OOD problem.
- mis_analysis.ipynb: Generates plots for the misclassification problem.
- mis_visualizations.ipynb: Creates visualizations for the misclassification problem.

### Notes on Trained Models
Due to a supplementary material size limit of 100MB, we cannot include the trained models in this repository. However, we are ready to provide them through any resource that maintains anonymity upon request.
expert-intelligence-toolbox 1.0.21
Collection of geospatial and transformational functions used by Expert Intelligence Lda.
Requires Python: >=3.7

# Expert Intelligence Toolbox

Expert Intelligence is a broadband research company. Our work involves geographic data across different granularities and classification systems.

This toolbox contains functions to help us do our work.

Mac users may need to install lxml, which will need to be downloaded separately first.

Internal users: Use 'instructions.md' for information on maintaining this package.

```
xcode-select --install
pip install lxml==4.9.2
```
pyhaloxml 3.1.2
Read and write the annotation files from Halo
Requires Python: >=3.10
Keywords: Halo, reader, annotation

[![Mypy](https://github.com/rharkes/pyhaloxml/actions/workflows/mypy.yml/badge.svg)](https://github.com/rharkes/pyhaloxml/actions/workflows/mypy.yml)
[![Black](https://github.com/rharkes/pyhaloxml/actions/workflows/black.yml/badge.svg)](https://github.com/rharkes/pyhaloxml/actions/workflows/black.yml)
[![Pytest](https://github.com/rharkes/pyhaloxml/actions/workflows/pytest.yml/badge.svg)](https://github.com/rharkes/pyhaloxml/actions/workflows/pytest.yml)
[![Docs](https://readthedocs.org/projects/pyhaloxml/badge/?version=latest&style=flat)](https://readthedocs.org/projects/pyhaloxml)
# PyHaloXML
Python code to read/write .annotation files from Halo. Can export as .geojson for further analysis.

## Examples
[Example 1](https://github.com/rharkes/pyhaloxml/blob/main/examples/example1.py) : Move rectangles to a different layer and save as `.annotation`.

[Example 2](https://github.com/rharkes/pyhaloxml/blob/main/examples/example2.py) : Check if annotations have holes in them and save as `.geojson`.

[Example 3](https://github.com/rharkes/pyhaloxml/blob/main/examples/example3.py) : Show the wkt representation of the shapely polygon.

[Example 4](https://github.com/rharkes/pyhaloxml/blob/main/examples/example4.py) : Create a .annotation file from coordinates.

## Installation
`pip install pyhaloxml`

## Note on version 3
The matching of negative to positive regions now needs to be done by the user after loading the data. The examples have been updated. This needed to happen because negative regions can be unmatched in Halo. This would cause errors when loading. 

## Loading speed
Rust is used to match the negative regions to positive regions, thanks to [Wim Pomp](github.com/wimpomp/)!

It takes 41 seconds to load a 30.9MB file with 856454 vertices in 5769 regions with 731 holes.

## Development
* Install [Rust](https://rustup.rs/)
* Clone the repository
* `pip install -e .`

### Notes on development
* The xml is relatively simple. There are Annotations and an annotation contains regions.
* Regions can be either positive or negative. However regions in an annotation are not hierarchical. So there is no telling what negative region should go with what positive region.
* This package expects a negative region to be fully enclosed by one positive region. The matching is done by taking a single point that is inside or on the border of the negative region and checking if it is inside a positive region.