neural-feature-importance 0.9.1
Variance-based feature importance for Neural Networks using callbacks for Keras and PyTorch
Requires Python: >=3.10

# neural-feature-importance

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Variance-based feature importance for deep learning models.

`neural-feature-importance` implements the method described in
[CR de Sá, *Variance-based Feature Importance in Neural Networks*](https://doi.org/10.1007/978-3-030-33778-0_24).
It tracks the variance of the first trainable layer using Welford's algorithm
and produces normalized importance scores for each feature.

## Features

- `VarianceImportanceKeras` — drop-in callback for TensorFlow/Keras models
- `VarianceImportanceTorch` — helper class for PyTorch training loops
- `MetricThreshold` — early-stopping callback based on a monitored metric
- Example scripts to reproduce the experiments from the paper

## Installation

```bash
pip install "neural-feature-importance[tensorflow]"  # for Keras
pip install "neural-feature-importance[torch]"       # for PyTorch
```

Retrieve the package version via:

```python
from neural_feature_importance import __version__
print(__version__)
```

## Quick start

### Keras

```python
from neural_feature_importance import VarianceImportanceKeras
from neural_feature_importance.utils import MetricThreshold

viann = VarianceImportanceKeras()
monitor = MetricThreshold(monitor="val_accuracy", threshold=0.95)
model.fit(X, y, validation_split=0.05, epochs=30, callbacks=[viann, monitor])
print(viann.feature_importances_)
```

### PyTorch

```python
from neural_feature_importance import VarianceImportanceTorch

tracker = VarianceImportanceTorch(model)
tracker.on_train_begin()
for epoch in range(num_epochs):
    train_one_epoch(model, optimizer, dataloader)
    tracker.on_epoch_end()
tracker.on_train_end()
print(tracker.feature_importances_)
```

## Example scripts

Run `scripts/compare_feature_importance.py` to train a small network on the Iris dataset
and compare the scores with a random forest baseline:

```bash
python compare_feature_importance.py
```

Run `scripts/full_experiment.py` to reproduce the experiments from the paper:

```bash
python full_experiment.py
```

### Convolutional models

To compute importances for convolutional networks, use
`ConvVarianceImportanceKeras` from `neural_feature_importance.conv_callbacks`.
`scripts/conv_visualization_example.py` trains small Conv2D models on the MNIST
and scikit‑learn digits datasets and displays per-filter heatmaps. An equivalent
notebook is available in ``notebooks/conv_visualization_example.ipynb``:

```bash
python scripts/conv_visualization_example.py
```

### Embedding layers

To compute token importances from embedding weights, use
`EmbeddingVarianceImportanceKeras` or `EmbeddingVarianceImportanceTorch` from
`neural_feature_importance.embedding_callbacks`.
Run `scripts/token_importance_topk_example.py` to train a small text classifier
on IMDB and display the most important tokens. A matching notebook lives in
``notebooks/token_importance_topk_example.ipynb``:

```bash
python scripts/token_importance_topk_example.py
```

## Development

After making changes, run the following checks:

```bash
python -m py_compile neural_feature_importance/callbacks.py
python -m py_compile "variance-based feature importance in artificial neural networks.ipynb" 2>&1 | head
jupyter nbconvert --to script "variance-based feature importance in artificial neural networks.ipynb" --stdout | head
```

## Citation

If you use this package in your research, please cite:

```bibtex
@inproceedings{DBLP:conf/dis/Sa19,
  author       = {Cl{\'a}udio Rebelo de S{\'a}},
  editor       = {Petra Kralj Novak and
                  Tomislav Smuc and
                  Saso Dzeroski},
  title        = {Variance-Based Feature Importance in Neural Networks},
  booktitle    = {Discovery Science - 22nd International Conference, {DS} 2019, Split,
                  Croatia, October 28-30, 2019, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {11828},
  pages        = {306--315},
  publisher    = {Springer},
  year         = {2019},
  url          = {https://doi.org/10.1007/978-3-030-33778-0\_24},
  doi          = {10.1007/978-3-030-33778-0\_24},
  timestamp    = {Thu, 07 Nov 2019 09:20:36 +0100},
  biburl       = {https://dblp.org/rec/conf/dis/Sa19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```

We appreciate citations as they help the community discover this work.

## License

This project is licensed under the [MIT License](LICENSE).