semvec/pypi-packages huggingface dataset:
aion-optimizer 0.1.1
An automated machine learning optimization package
Requires Python: >=3.8

# Aion Optimizer

A Python package for automated machine learning optimization.

## Installation

```bash
pip install aion_optimizer
```

## Usage

```python
from aion_optimizer import EnhancedSequentialOptimizer
import pandas as pd
from sklearn.model_selection import train_test_split

dataset_path = "..."

df = pd.read_csv(dataset_path)

optimizer = EnhancedSequentialOptimizer(
        task_type='classification',
        target_column='class', 
        generations=5,
        cv_folds=5,
        random_state=42,
        n_jobs=-1,
        verbosity=0,
        categorical_threshold=10
    )

train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)

# Fit the optimizer
optimizer.fit(train_df)

# Get the best model parameters and print them
results = optimizer.best_model_params(test_df)
print(results)

## Features
- Automatic model selection
- Hyperparameter optimization
- Support for:
  - Binary classification
  - Multiclass classification
  - Regression
- Simplified metrics reporting
- Cross-validation support
- Detailed results logging

## Requirements
- Python >=3.7
- NumPy >=1.19.0
- Pandas >=1.2.0
- Scikit-learn >=0.24.0
- SciPy >=1.6.0
ceo-framework 0.1.8
Conditional Equality Operator Framework for conditional transformations in Python
Requires Python: >=3.6


# CEO Framework

The **Conditional Equality Operator (CEO) Framework** is a Python package for conditionally applying transformations to data based on user-defined criteria. Designed to work with various data types (e.g., images, text, numerical data), it simplifies conditional transformations in ML, NLP, and image processing workflows.

## Installation

```bash
pip install ceo-framework
```

## Usage

```python
from ceo_framework import CEO, ValueAboveThreshold, Normalize

ceo = CEO()
ceo.add_condition(ValueAboveThreshold(10))
ceo.add_transformation(Normalize(scale=0.5))

data = 15
result = ceo.process(data)
print("Processed Result:", result)
```

## Features

- Define custom conditions for transformation.
- Apply transformations conditionally across data types.
- Easily extensible for ML, NLP, and image processing applications.

## License

This project is licensed under the MIT License.
front-matter-parser-py 0.0.1
Front matter parser for Python
Requires Python: >=3.8
Keywords: markdown, markdown-it, lexer, parser, development

# Overview

This is a simple plugin for mdit-py, a markdown parser for Python. It adds the
ability to load the front matter of a markdown file, which is a YAML block at the
beginning of the file. The front matter is parsed and stored in the `front_matter`
attribute of the `Mdit` object.