semvec/pypi-packages huggingface dataset:
autonova 1.0.5
An automated machine learning pipeline with all-at-one-click feature.
License: MIT
Requires Python: >=3.7

# AutoNova  

AutoNova is a modern **AutoML framework** that can handle everything from **data preprocessing to model training** in just **3 lines of code**.  
It is designed to save your time, boost productivity, and make Machine Learning workflows **effortless**.  

---

##  Features  

🔹 **Preprocessing Class**  
-  **Cleaning & Transforming** – Handle missing values, scaling, encoding  
-  **Feature Engineering** – Generate new useful features automatically  
-  **Feature Selection** – Select only the most important features  
-  **Balancing** – Fix imbalanced datasets with SMOTE  
-  **Splitting** – Train-test splits with ease  

🔹 **Model Training Class**  
-  Automated Model Selection & Training  
-  Hyperparameter Tuning with **Optuna**  
-  Performance Metrics (Accuracy, Precision, Recall, F1, ROC-AUC)  

---

##  Installation  

```bash
pip install autonova
```

##  Usage Description

```python 
from autonova.auto import AutoNova
import pandas as pd

df = pd.read_csv("star_classification.csv")
target_col = "class"

automl = AutoNova(data=df, target_col=target_col)
automl.go(use_gpu=False, fast_mode=False, cv_splits=5, n_trials=50)

print("Best Model:", mode.best_model)
print("Preprocessing Steps:", mode.preprocess_logic)
print("Score:", mode.score)
print("Train Data Shapes:", [x.shape for x in mode.train_data])
print("Test Data Shapes:", [x.shape for x in mode.test_data])
```
- `use_gpu=True` GPU for Optuna training
- `fast_mode=True` only Faster models
- `cv_splits=5` number of Splits
- `n_trials=50` number of Trials

##  Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change. Take use of `CONTRIBUTING.md` file to make contributions.        
Github Link : https://github.com/Sanjaypal1916/ProjectX

##  License

MIT License – do anything, just give credit.

##  Need Help?

If you’re stuck, don’t worry — we’ve got your back!  
- Open a [Discussion](../../discussions) for general questions.  
- Create an [Issue](../../issues) for bug reports or feature requests.  
- Or contact the maintainers directly.  

We’d love to see your contributions and help you along the way.  

---

✨ Keep learning, keep building, and keep innovating!  
💡 Together, we can make **AutoNova** even better.  

Best,  
**The AutoNova Team** 🚀
pydarkdiv 1.0.1
Python implementation of the DarkDiv R package for calculating dark diversity from co-occurrence data
License: MIT
Requires Python: >=3.11

# PyDarkDiv

Python implementation of the DarkDiv R package for calculating dark diversity from species co-occurrence data.

## Overview

PyDarkDiv estimates dark diversity - the unobserved portion of a site's species pool consisting of species that could potentially occur but are currently absent. This package implements the methods described in Carmona & Pärtel (2021).

**Based on:** DarkDiv R package by Carlos P. Carmona and Meelis Pärtel

## Installation

```bash
pip install pydarkdiv
```

## Quick Start

```python
import pandas as pd
import pydarkdiv as pdd

# Load your species data (sites × species matrix)
data = pd.read_csv('species_data.csv', index_col=0)

# Calculate dark diversity
dd = pdd.DarkDiv(data, method='Hypergeometric')

# Get results as DataFrames
dfs = dd.to_dataframes()
dark_diversity = dfs['dark']
species_pool = dfs['pool']

# Summary statistics
dark_richness = (dark_diversity > 0.5).sum(axis=1)
print(f"Average dark diversity: {dark_richness.mean():.1f} species")
```

## Methods

- **Hypergeometric** (recommended): Compares observed vs. expected co-occurrences
- **RawBeals**: Beals smoothing for occurrence probabilities  
- **Favorability**: Favorability-corrected Beals values
- **ThresholdBeals**: Binary predictions using thresholds

## Usage Examples

See `usage_examples.py` for detailed examples including:

- Basic usage
- Method comparisons
- Reference data usage
- Abundance weighting

## Requirements

- Python ≥ 3.8
- numpy ≥ 1.21.0
- pandas ≥ 1.3.0
- scipy ≥ 1.7.0

## Reference

Carmona, C.P. & Pärtel, M. (2021). Estimating probabilistic site-specific species pools and dark diversity from co-occurrence data. *Global Ecology and Biogeography*, 30(1), 316-326. [DOI link](https://doi.org/10.1111/geb.13203)

## License

MIT License