semvec/pypi-packages huggingface dataset:
meiga 1.10.1
A simple, typed and monad-based Result type for Python
Requires Python: >=3.8
Keywords: Monad, Result, Typed, Typing, result-type

# meiga 🧙

[![version](https://img.shields.io/github/release/alice-biometrics/meiga/all.svg)](https://github.com/alice-biometrics/meiga/releases)
[![ci](https://github.com/alice-biometrics/meiga/workflows/ci/badge.svg)](https://github.com/alice-biometrics/meiga/actions)
[![pypi](https://img.shields.io/pypi/dm/meiga)](https://pypi.org/project/meiga/)
[![codecov](https://codecov.io/gh/alice-biometrics/meiga/branch/main/graph/badge.svg?token=BX1IZJZLJQ)](https://codecov.io/gh/alice-biometrics/meiga)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![license](https://img.shields.io/github/license/alice-biometrics/meiga.svg)](https://github.com/alice-biometrics/meiga/blob/main/LICENSE)
[![versions](https://img.shields.io/pypi/pyversions/meiga.svg)](https://github.com/alice-biometrics/meiga)

<img src="https://github.com/alice-biometrics/custom-emojis/blob/master/images/alice_header.png?raw=true" width=auto>

---

**Documentation**: <a href="https://alice-biometrics.github.io/meiga/" target="_blank">https://alice-biometrics.github.io/meiga/</a>

**Source Code**: <a href="https://github.com/alice-biometrics/meiga" target="_blank">https://github.com/alice-biometrics/meiga</a>

---

## What is meiga 🧙?

`meiga 🧙` is a Python µframework that provides a simple, fully typed, monad-based result type ➡️ `Result[Value, Error]`.

## How could meiga 🧙 help me?

`meiga 🧙` provides a simple and clear way of handling errors in Python without using `Exceptions`. This package improves the Dev Experience as it allows to know all possible typed responses. With Meiga 🧙 your IDE will help you much more.

## Installation 💻

```console
pip install meiga
```

## Contribute

We'd love you to contribute to *meiga* 🥳🥳🥳🥳🥳🥳️️!

For more information, check our [documentation](https://alice-biometrics.github.io/meiga/contributing/)

## Contact 📬

support@alicebiometrics.com
typarse 4.2.0
Simple type-based argument parsing
Requires Python: >=3.8

# Typarse

This is a small project born out of my frustration with simple argument parsing in Python.

Not only do I have to instantiate some object whose name I can never remember, then I get way too many 
function parameters to get them right... It's a mess. And I don't even need half the features.

So this is an attempt at streamlining this process while simultaneously promoting some better type safety, by using the
magic of Python type hints! 

Really all the magic here is happening in the BaseParser class. You just need to subclass it, add a few typed parameters,
perhaps with some extra information in dictionaries... and you're done! For examples, just look at, well, examples.

## Supported types

First of all, all the basic types supported by argparse are also supported here. Things like: `str`, `int`, `float`. `bool`s are automatically interpreted as flags, False by default. Each type can be wrapped in a `List` to support reading them like `--list 1 2 3 4`. Each type can also be made `Optional` which makes it, well, optinal.


## Simple illustrative example

```python
from typarse import BaseParser
from typing import List


class Parser(BaseParser):
    nums: List[int]
    square: bool
    default: int = 0

    _abbrev = {
        "nums": "n",
        "square": "s",
        "default": "d"
    }

    _help = {
        "nums": "List of numbers to sum",
        "square": "Whether the result should be squared",
        "default": "Initial value, added to the sum"
    }



args = Parser()

print(args.default + sum(args.nums) ** (1+args.square))
```

# Functionality 2: Config

In the spirit of the library, I added a config management component. 
Similarly to parsing, you can define a config using type annotations on a class -- also in a nested manner.

Example:
```python
from typarse import BaseConfig
from typing import List 

class Config(BaseConfig):
    rate: float = 0.1
    amount: float = 0.01
    limit: float = 1.
    class PolicyConfig(BaseConfig):
        layers: List[int] = [32, 32, 32]
        activation: str = "relu"

config = {
    "rate": 0.5,
    "PolicyConfig": {
        "layers": [64, 64]
    }
}

```

# Installation

It's on PyPi so install it with `pip install typarse`, `poetry add typarse` or whatever you want. If you want to install it from the source, it's pure Python so I'm sure you can figure it out.
HolaCabezon 0.1.0
Search course in hack4you academy


# Hack4U Academy Courses Library

Una bibiblioteca python para consultar cursos de hack4you.

## Cursos dsiponibles:

- Introducción a Linux [15 horas]
- Personalización de Linux [3 horas]
- Introducción al Hacking [53 horas]

## Instalación

Instala el paquete usando pip3:

```python3
pip install hack4you
```
## Uso básico:

### Listar todos los cursos

```python
from hack4you import list_courses

for course in list_courses:
    print(course)
```

### Obtener curso por nombre

```python
from hack4you import get_course_by_name
course = get_course_by_name("Introducción a Linux)
print(course)
```

### Total duration

```python3
from hack4you import total_duration
    print(f"El computo de horas totales es de :{total_duration()}")
al360-trustworthyai 0.0.2
SDK API to explain models, generate counterfactual examples, analyze causal effects and analyze errors in Machine Learning models.
Requires Python: >=3.7

# AL360° Trustworthy AI Model Analysis SDK for Python

### This package has been tested with Python 3.7, 3.8, 3.9 and 3.10

The AL360° Trustworthy AI Model Analysis SDK enables users to analyze their machine learning models in one API. Users will be able to analyze errors, explain the most important features, compute counterfactuals and run causal analysis using a single API.

Highlights of the package include:

- `explainer.add()` explains the model
- `counterfactuals.add()` computes counterfactuals
- `error_analysis.add()` runs error analysis
- `causal.add()` runs causal analysis

### Supported scenarios, models and datasets

`al360_trustworthyai` supports computation of AL360° Trustworthy AI insights for `scikit-learn` models that are trained on `pandas.DataFrame`. The `al360_trustworthyai` accept both models and pipelines as input as long as the model or pipeline implements a `predict` or `predict_proba` function that conforms to the `scikit-learn` convention. If not compatible, you can wrap your model's prediction function into a wrapper class that transforms the output into the format that is supported (`predict` or `predict_proba` of `scikit-learn`), and pass that wrapper class to modules in `al360_trustworthyai`.

Currently, we support datasets having numerical and categorical features. The following table provides the scenarios supported for each of the four responsible AI insights:-

| RAI insight | Binary classification | Multi-class classification | Multilabel classification | Regression | Timeseries forecasting | Categorical features | Text features | Image Features | Recommender Systems | Reinforcement Learning |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | -- |
| Explainability | Yes | Yes | No | Yes | No | Yes | No | No | No | No |
| Error Analysis | Yes | Yes | No | Yes | No | Yes | No | No | No | No |
| Causal Analysis | Yes | No | No | Yes | No | Yes (max 5 features due to expensiveness) | No | No | No | No |
| Counterfactual | Yes | Yes | No | Yes | No | Yes | No | No | No | No |


The source code can be found here:
https://github.com/affectlog360/affectlog360/tree/main/al360_trustworthyai