semvec/pypi-packages huggingface dataset:
pdutility 0.1.4
Pandas utility library - major release pending

# **Pandas utility library&mdash;an easier way to process data in dataframes.**

## Overview
This library offers enhanced tools for the pre-processing and analysis of structured data.

## License Information
This software is available under a dual-license model:
- Open-source: Licensed under the MIT License for non-commercial use, educational purposes, or personal projects.
- Commercial: For use in commercial products, services, or environments, a Commercial License is required.

See [LICENSE](https://github.com/techds/structured-data-processing-demos/blob/main/LICENSE) for the MIT open-source license terms, and [COMMERCIAL LICENSE](https://github.com/techds/structured-data-processing-demos/blob/main/commercial-license.md) for terms governing commercial use.

## Attribution Notice
This software primarily relies on the following open-source libraries:

1. **Pandas**: This library is used for data manipulation and analysis. It is licensed under the BSD 3-Clause License. You can find more information and the full license text at [https://pandas.pydata.org](https://pandas.pydata.org).

2. **Scikit-Learn**: This library is utilized for machine learning algorithms and tools. It is licensed under the BSD 3-Clause License. More information and the full license text can be found at [https://scikit-learn.org](https://scikit-learn.org).

3. **NumPy**: This library is used for numerical computations and array processing. It is licensed under the BSD 3-Clause License. More information and the full license text can be found at https://numpy.org.

4. **Seaborn**: This library provides advanced statistical data visualization tools based on Matplotlib. It is licensed under the BSD 3-Clause License. For more information, visit https://seaborn.pydata.org.

This package is developed independently and is not directly affiliated with or endorsed by these libraries.
scilite 0.0.1
Sci-Lite is Light version of Supervised Machine Learning Model
Requires Python: >=3.6

# **SciLite**


**SciLite** provides a streamlined solution for evaluating various supervised machine learning algorithms, simplifying the data scientist's workflow by automating model integration and evaluation. With just a few lines of code, SciLite enables quick access to essential machine learning evaluations, saving valuable time and effort.

### Installation

To install the package, run:

'''bash
pip install scilite
'''

### Usage

Basic example of how to use the package in Python:

'''bash
from scilite.univariate.regression import Regression as reg
result = reg(X_train, X_test, y_train, y_test)
print(result)

'''

### Features

- **One-Click Model Integration:** Supports easy integration and evaluation of multiple supervised machine learning models, whether for regression or classification.
- **Efficient Model Evaluation:** Simplifies machine learning model evaluations with standardized output, aiding in faster analysis and comparisons.

### Contributing

Contributions to SciLite are warmly welcomed! If you'd like to contribute, please fork the repository and submit a pull request or open an issue to discuss improvements.

### Contact
For any questions or issues, feel free to reach out to:

Author: Ayush Jain Sparsh
Email: ayushjainsparsh2004.ajs@gmail.com
EPIC-API-Client 2024.1.1
EPIC API Client including authentication flow and some wrapped API calls
Requires Python: >=3.8

# EPIC-API-Connector

## Installation

use pip install EPIC-API-Client
see example.py for usage and setup .env accordingly.
tip: use uv pip for faster pip (pip install uv)!

## functions

### handle_external_model_scores

Send predictive model scores back to the Epic Cognitive Computing Platform for filing.
```
:param model_id: str, the ECCP model ID the scores are for
:param job_id: str, the autogenerated job ID for the evaluation on ECCP
:param output_type: str, the type of output for the predictive model
:param server_version: str, the server version of the predictive context
:param session_id: str, the session ID of the predictive context
:param entity_ids: list, a list of dictionaries with ID and Type for the entity
:param outputs: dict, the output values of the predictive model
:param raw: dict, optional, raw features used to calculate the scores
:param predictive_context: dict, optional, additional context information for the predictive model

:return: dict, the response from the Epic Cognitive Computing Platform
```
example outputs
```python
{
    "Output_Name": {
        "Scores": {
            "Score_Name_1" : { "Values": [val1, val2,.., valN] },
            "Score_Name_2" : { "Values": [val1, val2,.., valN] }
        },
        "Features": {
            "Feature1": { "Contributions":[contrib1, contrib2, ..., contribN]},
            "Feature2": { "Contributions":[contrib1, contrib2, ..., contribN]}
        }
    }
}
```
example raw
```python
{
    "Additional_Feature_1" : { "Values": [val1, val2,.., valN] },
    "Additional_Feature_2" : { "Values": [val1, val2,.., valN] }
}
```
scrapper-boilerplate 0.2.13
Scrapping/Automating tools, userSwitching, anti-bot detection and more...

# scrapper-boilerplate


Scrapping functions and for making easy for further scrapping

## instalation

### (Optional) install enviroment

make sure that you have python 3.10 installed

then install:

```python
pip install virtualenv
```

then install the enviroment and activate:

```python
python -m venv venv
.\venv\scripts\activate
```

### Install the dependêncies:

Install chromedriver [click here](https://chromedriver.chromium.org/downloads), and get the path location.

Create .env file with:

- chromedriver location
- token of telegram bot (if you use telegram function)

Example provided in ".env.example"

install the packages:

```python
pip install -r requirements.txt
```
and activate:
 
```python
python index.py
```

### setup enviroment

create ".env" file with following lines:

```env
CHROMEDRIVER_PATH="Insert your chromedriver path"
TELEGRAM_TOKEN="Insert your telegram token..."
```

## Usage 

```python
from time import sleep
from utils.setup import setSelenium
import os

ROOT_DIR = os.path.dirname(os.path.abspath(__file__))

def main():
    # import selenium initialization and browser it
    driver = setSelenium()
    driver.get('https://www.google.com')
    sleep(10)
    driver.quit()

if __name__ == "__main__":
    main()

```
model2graph 0.1.0
A package to convert deep learning models into graph structures
Requires Python: >3.10

# model2graph

## install packages
used python version == 3.11
```shell
# create conda environment
conda create -n cuml -c rapidsai -c conda-forge -c nvidia cudf=24.08 cuml=24.08 python=3.11 'cuda-version>=12.0,<=12.5'

# install packages from requirements.txt
pip install -r requirements.txt

```