semvec/pypi-packages huggingface dataset:
trustworthyai 0.0.6
SDK API to explain models, generate counterfactual examples, analyze causal effects and analyze errors in Machine Learning models.
Requires Python: >=3.7

# Trustworthy AI Model Analysis SDK for Python

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

The 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

`trustworthyai` supports computation of Trustworthy AI insights for `scikit-learn` models that are trained on `pandas.DataFrame`. The `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 `trustworthyai`.

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

| TAI 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/affectlog/trustworthy-ai-toolbox/tree/main/trustworthyai
affectlog-widgets 0.0.6
Interactive visualizations to assess fairness, explain models, generate counterfactual examples, analyze causal effects and analyze errors in Machine Learning models.
Requires Python: >=3.7

# AffectLog Widgets

This repository contains the `affectlog_widgets` library, which is used for creating interactive, visual Trustworthy AI tools. This project provides a set of widgets for model assessment, fairness analysis, error detection, and more, built on top of machine learning frameworks.

## Requirements for Development

To contribute to `affectlog_widgets`, make sure to install the following dependencies.

### Testing Dependencies

For running tests with code coverage and mocking, use the following:

```bash
pytest==7.0.1
pytest-cov
pytest-mock==3.6.1
```

### Parser Dependencies

For parsing requirements, use:

```bash
requirements-parser==0.2.0
```

### Package Building

Ensure you have the necessary tools for building the package:

```bash
wheel
```

### Machine Learning & Fairness Libraries

Install the following libraries to enable machine learning and fairness evaluation tools:

```bash
fairlearn==0.7.0
ml-wrappers>=0.4.0
sktime
pmdarima
```

**Note**: For Windows users running Python 3.7, add the following fix for `joblib` compatibility:

```bash
joblib<1.3.0; python_version <= '3.7' and sys_platform == 'win32'
```

### Notebook Testing

To test notebooks and perform notebook validation, install:

```bash
nbformat
papermill
scrapbook
jupyter
nbval
```

### Documentation Dependencies

For generating documentation, install the following:

```bash
docutils<=0.19
sphinx==5.0.2
sphinx-gallery==0.10.0
pydata-sphinx-theme==0.7.2
```

## Setting up the Development Environment

Follow these steps to set up your local development environment:

1. **Clone the repository**:
    ```bash
    git clone <repository_url>
    cd affectlog_widgets
    ```

2. **Install Dependencies**:
    You can install all dependencies via pip:
    ```bash
    pip install -r requirements.txt
    ```

3. **Run Tests**:
    To ensure everything is working, run the tests:
    ```bash
    pytest
    ```

## Local Installation

For local development, ensure that `trustworthyai` is available in your project. You can use the relative path for local development:

```bash
../trustworthyai/.
```

Add this path when running your local environment so the necessary modules can be accessed.

## Contributing

If you're interested in contributing, please create a feature branch from `main`, make your changes, and open a pull request. Ensure that all tests pass and documentation is updated where necessary.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
langtimezone 0.1.2a0
A Python library for extracting language and timezone based on country code.
Requires Python: >=3.6

# LangTimezone

LangTimezone is a Python library to extract language and timezone based on country code.

## Installation

You can install this package using pip:

```bash
pip install langtimezone
```

#How to Use
Here's a simple example of how to use the LanguageTimezoneExtractor class to get the language and timezone based on a country code:

```python
from langtimezone import LanguageTimezoneExtractor

# Create an instance of the extractor
extractor = LanguageTimezoneExtractor()

# Get language and timezone for a given country code
result = extractor.get_language_and_timezone(98)  # 98 is the country code for Iran

# Print the results
print(f"Language Code: {result['lang_code']}")
print(f"System Language Code: {result['system_lang_code']}")
print(f"Timezone: {result['timezone']}")
```