semvec/pypi-packages huggingface dataset:
GRLMerger 0.2.4
GRL Merger is a package that integrates two GRL models written in TGRL syntax into one GRL model.

# GRL Merger
This is a package used to merge two GRL models into one consolidated GRL model. The GRL models have to be written in a textual-GRL form (TGRL).

## Installation

Run the following to install:

```python
pip install GRLMerger
```


## Usage

```python
from grlmerger import startGRLMerger

# The input files must be in the same directory
startGRLMerger('model_1.xgrl', 'model_2.xgrl')

# It generates the input models in xlsx format, merged model in xgrl format, merged constructs in xlsx format, and conflict and error cases in xlsx format.


```
tabular-ml 0.0.2
This library wraps popular tabular regression/classification model enabling rapid evaluation and optimization.
Requires Python: >=3.11
Keywords: machine-learning,pipeline,factory,tabular,regression,classification

[![Pre-Commit Status](https://github.com/xaviernogueira/Tabular_ML/actions/workflows/pre-commit.yml/badge.svg)](https://github.com/xaviernogueira/Tabular_ML/actions/workflows/pre-commit.yml)
[![Tests Status](https://github.com/xaviernogueira/Tabular_ML/actions/workflows/tests.yml/badge.svg)](https://github.com/xaviernogueira/Tabular_ML/actions/workflows/tests.yml)
[![Coverage](https://codecov.io/gh/xaviernogueira/Tabular_ML/graph/badge.svg)](https://codecov.io/gh/xaviernogueira/Tabular_ML)

# `tabular_ml` - tabular machine learning simplified!
I've packaged and open sourced my personal machine learning tools to speed up your next data science project.

Train, evaluate, ensemble, and optimize hyperparameters from a standardized interface.

![repo_schematic](images/readme_image.png)

## Key Features
* Train models efficiently without worrying about library differences! `tabular_ml` implements library specific, performance oriented, patterns/classes under-the-hood (i.e., `xgboost.DMatrix -> xgboost.Booster`).
* Automate the K-Fold evaluation process across multiple models simultaneously (including ensembles).
* Rapidly optimize hyperparameters using [`optuna`](https://optuna.org/). Leverage our built-in parameter search spaces, or adjust to your needs.
* Plugin-able. Write your own plugins to extend functionality without forking (and consider contributing your plugins!).

**For full documentation see our GitHub ReadMe [here](https://github.com/xaviernogueira/Tabular_ML).**