semvec/pypi-packages huggingface dataset:
al360-trustworthyai-vision 0.3.8
SDK API to assess image Machine Learning models.
Requires Python: >=3.7

# AL360° Trustworthy AI Vision SDK for Python

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

The AL360° Trustworthy AI Vision SDK enables users to analyze their machine learning models for computer vision in one API. Users will be able to analyze errors, explain the most important features, and understand their data using a single API.

Highlights of the package include:

- `explainer.add()` explains the model

### Supported scenarios, models and datasets

The AL360° Trustworthy AI Vision SDK supports multiclass classification models on image data currently.

The open source code for the visualization dashboard can be found here:
https://github.com/affectlog360/al360-trustworthy-ai-widgets
al360-trustworthyai-text 0.2.7
SDK API to assess text Machine Learning models.
Requires Python: >=3.7

# AL360° Trustworthy AI Text SDK for Python

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

The AL360° Trustworthy AI Text sdk enables users to analyze their machine learning models for text data in one API. Users will be able to analyze errors, explain the most important features, and understand their data using a single API.

Highlights of the package include:

- `explainer.add()` explains the model

### Supported scenarios, models and datasets

The AL360° Trustworthy AI Text SDK supports multiclass classification and question answering models on text data currently.

The open source code for the visualization dashboard can be found here:
https://github.com/affectlog360/affectlog360
doona-taos-edge-model 0.2.0
TAOS Edge Model 예측 패키지
Requires Python: >=3.6


## 사용 방법

다음과 같이 패키지를 임포트하고 사용할 수 있습니다:

```python
from doona_taos_edge_model.model_predictor import ModelPredictor
predictor = ModelPredictor('model_uuid.dill')
```

### 테스트
```python
df = predictor.load_test_csv('sample_uuid.csv')
sample_df = predictor.get_sample(df, 1)
result = predictor.pipeline_and_predict(sample_df)
print(result)
```

### 예측 수행
```python
result = predictor.pipeline_and_predict(df)
```

### 결과 출력
```python
print(result)
```

### 리포트 출력
```python
predictor.print_report()
al360-taiutils 0.4.2
Common basic utilities used across various RAI tools
Requires Python: >=3.7

# AL360° Trustworthy AI Utilities for Python

### This package has been tested with Python 3.7, 3.8, 3.9, 3.10 and 3.11

The AL360° Trustworthy AI Utilities package contains common functions shared across various RAI tools, including fairlearn, interpret-community, al360_trustworthyai, al360_taiwidgets and other packages, as well as notebook examples.

Please see the main documentation website:
https://responsibleaitoolbox.ai/

The open source code can be found here:
https://github.com/affectlog360/al360-trustworthy-ai-widgets
DeepSomaticCopy 0.0.26
Copy number calling on single cell DNA sequencing data
Requires Python: >=3.7

# DeepSomaticCopy

The pip package "DeepSomaticCopy" implements our "DeepCopy" algorithm for single cell copy number calling.
DeepCopy specifically works on single cell DNA sequencing data of tumors, and reconstructs copy number profiles using an evolution-aware deep reinforcement learning algorithm. 
Additional informaiton is provided on our [GitHub page](https://github.com/elkebir-group/DeepCopy).
al360-tai-test-utils 0.4.2
Common basic test utilities used across various RAI tools
Requires Python: >=3.7

# AL360° Trustworthy AI test utilities for Python

### This package has been tested with Python 3.7, 3.8, 3.9, 3.10 and 3.11

The AL360° Trustworthy AI Test Utilities package contains common testing utilities and functions shared across various RAI tools, including fairlearn, interpret-community, al360_trustworthyai, al360_taiwidgets, ml-wrappers and other packages.

Please see the main documentation website:
https://responsibleaitoolbox.ai/

The open source code can be found here:
https://github.com/affectlog360/affectlog360
al360-tai-core-flask 0.7.6
AL360° Trustworthy AI Core Flask Wrapper

# al360_tai_core_flask

Provide environment detection for enabling Flask server without configuring at instantiation. Environments are implemented as classes that will either return None if the environment check fails, or return an object with all the appropriate environment settings if the check passes. The flask service can then use it to build out urls, attach required headers, and render inline visualizations with the correct methods.

## Installation

`pip install -e .`

## Usage

`from al360_tai_core_flask import FlaskHelper`
`flask_service = FlaskHelper(ip=<ip-to-listen-on>, port=<port-to-listen-on>)`

If `ip` is not specified then it listens on `localhost` by default.
If `port` is not specified then it finds an open port in the range 8704 to 8993
and listens on it.

To register a function to listen on an route:

```
@flask_service.app.route("/your-api-route/<int:param_name>", methods=["GET", "POST"])
def my_func(param_name):
    return something
```

## Release Process

1. Increment the version number in `setup.py`
2. Activate your virtual environment.
3. Run the `release.sh` script.
al360-nlp-feature-extractors 0.1.0
NLP Feature Extractors

# NLP Feature Extractors SDK for Python

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

The NLP Feature Extractors project enables the tabular components in the RAI tools to be applied to Natural Language Processing (NLP)
tasks on unstructured text.

Please see the main documentation website:
https://responsibleaitoolbox.ai/

The open source code can be found here:
https://github.com/affectlog360/al360-trustworthy-ai-widgets
django-tui 24.5
Inspect and run Django Commands in a text-based user interface (TUI).
Requires Python: >=3.8
Keywords: django, textual, tui


![Screenshot 2023-10-24 at 11 27 56](https://github.com/anze3db/django-tui/assets/513444/54bc6c84-267c-4e28-9d49-8b30391470ea)

# django-tui

Inspect and run Django Commands in a text-based user interface (TUI), built with [Textual](https://github.com/Textualize/textual) & [Trogon](https://github.com/Textualize/trogon).

[![PyPI - Version](https://img.shields.io/pypi/v/django-tui.svg)](https://pypi.org/project/django-tui)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/django-tui.svg)](https://pypi.org/project/django-tui)

-----

**Table of Contents**

- [Demo](#demo)
- [Installation](#installation)
- [Running](#running)
- [License](#license)

## 🎬 Demos

### Running Django Commands

https://github.com/anze3db/django-tui/assets/513444/fc84247a-9f4b-4017-9ae4-3f10fe9d9557

### Running ORM Queries in Interactive Django Shell

https://github.com/anze3db/django-tui/assets/513444/8a056da8-85a8-4086-9fa8-433b7346e787

## Installation

```console
pip install django-tui
```

Add `"django_tui"` to your `INSTALLED_APPS` setting in `settings.py` like this:


```python
INSTALLED_APPS = [
    ...,
    "django_tui",
]
```

Now you can run the TUI with:

```console
python manage.py tui
```

## License

`django-tui` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.

## Looking for Django Admin in your termain?

Checkout [@valberg](https://github.com/valberg)'s [django-admin-tui](https://github.com/valberg/django-admin-tui) project!
al360-erroranalysis 0.5.4
Core error analysis APIs
Requires Python: >=3.7

# Error Analysis SDK for Python

### This package has been tested with Python 3.7, 3.8, 3.9, 3.10 and 3.11

The error analysis sdk enables users to get a deeper understanding of machine learning model errors. When evaluating a machine learning model, aggregate accuracy is not sufficient and single-score evaluation may hide important conditions of inaccuracies. Use Error Analysis to identify cohorts with higher error rates and diagnose the root causes behind these errors.

Highlights of the package include:

- The error heatmap to investigate how one or two input features impact the error rate across cohorts
- The decision tree surrogate model trained on errors to discover cohorts with high error rates across multiple features. Investigate indicators such as error rate, error coverage, and data representation for each discovered cohort.

Please see the main documentation website:
https://al360_erroranalysis.ai/

Auto-generated sphinx API documentation can be found here:
https://al360_erroranalysis.ai/al360_taiwidgets.html

The open source code can be found here:
https://github.com/affectlog360/al360-trustworthy-ai-widgets
srelu 0.1.0
A library for the SReLU activation function.
Requires Python: >=3.6

The SReLU (Smooth Rectified Linear Unit) is an activation function designed to improve deep learning model performance by providing smooth, differentiable gradients across both positive and negative input domains. Traditional activation functions like ReLU (Rectified Linear Unit) have been widely used for their simplicity and computational efficiency, but they come with some drawbacks, especially when dealing with negative input values, where the gradient becomes zero (leading to the "dying ReLU" problem). SReLU addresses these issues while preserving the benefits of ReLU for positive inputs.
nwos 0.3
A library for working with prime and composite numbers
Requires Python: >=3.6

# NWOS: Python Library for Prime and Composite Number Checking

The `nwos` library is a powerful and easy-to-use Python package designed to work with **prime** and **composite** numbers. It provides efficient algorithms to check whether individual numbers are prime or composite and can also handle batch operations for lists of numbers.

## Table of Contents
- [Installation](#installation)
- [Features](#features)
- [Usage](#usage)
  - [Checking a Single Number](#example-1-checking-a-single-number)
  - [Checking Composite Numbers](#example-2-checking-if-a-number-is-composite)
  - [Checking a List of Numbers](#example-3-checking-a-list-of-numbers)
- [Handling Errors](#handling-errors)
- [Contributing](#contributing)
- [License](#license)

## Installation

You can easily install `nwos` using pip from the Python Package Index (PyPI):

```bash
pip install nwos