Metadata-Version: 2.4
Name: seq_hybrid_detector
Version: 0.1.0
Summary: A sequential hybrid anomaly detection framework combining PyTorch GRUs and Isolation Forests.
Author-email: Your Name <your.email@example.com>
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: torch>=2.0.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: joblib>=1.1.0
Requires-Dist: matplotlib>=3.4.0
Requires-Dist: seaborn>=0.11.0
Dynamic: license-file

# seq_hybrid_detector

`seq_hybrid_detector` is a small Python package scaffold for sequence hybrid detection workflows.

## What is included

The repository currently provides a clean `src/` layout with starter modules for loading data, defining core models, and wiring a simple pipeline.

## Project layout

```text
seq_hybrid_detector/
├── LICENSE
├── README.md
├── pyproject.toml
└── src/
	└── seq_hybrid_detector/
		├── __init__.py
		├── data_engine.py
		├── models.py
		└── pipeline.py
```

## Installation

From the project root, install the package in editable mode during development:

```bash
pip install -e .
```

## Modules

- `data_engine.py` contains basic sequence loading and normalization helpers.
- `models.py` defines lightweight dataclasses for input samples and predictions.
- `pipeline.py` provides a starter pipeline class for fitting and predicting.

## Status

This is a scaffold, not a finished detector. The modules are in place for you to extend with real preprocessing, model training, and scoring logic.
