Metadata-Version: 2.4
Name: phyto_nas_tsc
Version: 0.1.0
Summary: Phyto Neural Architecture Search for Time Series Classification
Home-page: https://github.com/carmelyr/Phyto-NAS-T
Author: Carmely Reiska
Author-email: reiskacarmely@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.19.0
Requires-Dist: pandas>=1.0.0
Requires-Dist: torch>=1.8.0
Requires-Dist: pytorch-lightning>=1.4.0
Requires-Dist: scikit-learn>=1.2.0
Requires-Dist: tqdm>=4.0.0
Provides-Extra: dev
Requires-Dist: pytest>=6.0.0; extra == "dev"
Requires-Dist: black>=21.0; extra == "dev"
Requires-Dist: flake8>=3.9.0; extra == "dev"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: provides-extra
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Phyto-NAS-TSC

Neural Architecture Search for Time Series Classification

## Installation

```bash
pip install phyto-nas-tsc

## Quickstart

```python
import numpy as np
from phyto_nas_tsc import fit

# Synthetic data
X = np.random.rand(100, 10, 1)  # 100 samples, 10 timesteps, 1 feature
y = np.eye(2)[np.random.randint(0, 2, 100)]  # Binary classification

# Run optimization
results = fit(X, y, generations=3, population_size=5)
print(f"Best Architecture: {results['architecture']}")
```
