Metadata-Version: 2.4
Name: aieng-interp-utils
Version: 1.0.0
Summary: Helper modules for AI Engineering Interpretability Bootcamp implementations
Author-email: Vector AI Engineering <ai_engineering@vectorinstitute.ai>
License-Expression: MIT
Requires-Python: >=3.10
Requires-Dist: captum>=0.7.0
Requires-Dist: dice-ml>=0.9
Requires-Dist: interpret>=0.6.9
Requires-Dist: lime>=0.2.0.1
Requires-Dist: matplotlib>=3.10.0
Requires-Dist: numpy>=1.26.4
Requires-Dist: pandas>=2.2.3
Requires-Dist: scikit-learn>=1.6.1
Requires-Dist: seaborn>=0.13.2
Requires-Dist: shap>=0.46.0
Requires-Dist: torch>=2.6.0
Description-Content-Type: text/markdown

# AI Engineering Interpretability Utils

Helper modules for AI Engineering Interpretability Bootcamp implementations.

This package provides reusable utilities for:
- Loading and preprocessing common datasets (Adult, Diabetes, Bank Marketing, Gas Turbine)
- Model training and evaluation helpers
- Visualization utilities for interpretability

## Installation

```bash
pip install aieng-interp-utils
```

## Usage

```python
from aieng.interp.data import load_adult_dataset, preprocess_adult_data
from aieng.interp.models import train_pytorch_model, get_device
from aieng.interp.visualization import plot_confusion_matrix, plot_roc_curve

# Load and preprocess data
df = load_adult_dataset("path/to/data")
X_train, X_test, y_train, y_test = preprocess_adult_data(df)

# Train model
device = get_device()
model = train_pytorch_model(model, X_train, y_train, device=device)

# Visualize results
plot_roc_curve(y_test, y_pred_proba)
plot_confusion_matrix(y_test, y_pred)
```

## License

MIT
