DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) that are tractable and exact representations for the modelled probability distributions. The availability of a representative selection of DPMs in a single library makes it possible to combine them in a straightforward manner, a common practice in deep learning research nowadays. In addition, it includes efficiently implemented learning techniques, inference routines, statistical algorithms, and provides high-quality fully-documented APIs. The development of DeeProb-kit will help the community to accelerate research on DPMs as well as to standardise their evaluation and better understand how they are related based on their expressivity.
- Inference algorithms for SPNs. 1 2
- Learning algorithms for SPNs structure. 1 3 4 2 5
- Chow-Liu Trees (CLT) as SPN leaves. 6
- Cutset Networks (CNets) with various learning criteria. 7
- Batch Expectation-Maximization (EM) for SPNs with arbitrarily leaves. 8 9
- Structural marginalization and pruning algorithms for SPNs.
- High-order moments computation for SPNs.
- JSON I/O operations for SPNs and CLTs. 2
- Plotting operations based on NetworkX for SPNs and CLTs. 2
- Randomized And Tensorized SPNs (RAT-SPNs). 10
- Deep Generalized Convolutional SPNs (DGC-SPNs). 11
- Masked Autoregressive Flows (MAFs). 12
- Real Non-Volume-Preserving (RealNVP) flows. 13
- Non-linear Independent Component Estimation (NICE) flows. 14
The collection of implemented models is summarized in the following table.
| Model | Description |
|---|---|
| Binary-CLT | Binary Chow-Liu Tree (CLT) |
| Binary-CNet | Binary Cutset Network (CNet) |
| SPN | Vanilla Sum-Product Network |
| MSPN | Mixed Sum-Product Network |
| XPC | Random Probabilistic Circuit |
| RAT-SPN | Randomized and Tensorized Sum-Product Network |
| DGC-SPN | Deep Generalized Convolutional Sum-Product Network |
| MAF | Masked Autoregressive Flow |
| NICE | Non-linear Independent Components Estimation Flow |
| RealNVP | Real-valued Non-Volume-Preserving Flow |
The library can be installed either from PIP repository or by source code.
# Install from PIP repository
pip install deeprob-kit# Install from `main` git branch
pip install -e git+https://github.com/deeprob-org/deeprob-kit.git@main#egg=deeprob-kitThe documentation is generated automatically by Sphinx using sources stored in the docs directory.
A collection of code examples and experiments can be found in the examples and experiments directories respectively. Moreover, benchmark code can be found in the benchmark directory.
@misc{loconte2022deeprob,
doi = {10.48550/ARXIV.2212.04403},
url = {https://arxiv.org/abs/2212.04403},
author = {Loconte, Lorenzo and Gala, Gennaro},
title = {{DeeProb-kit}: a Python Library for Deep Probabilistic Modelling},
publisher = {arXiv},
year = {2022}
}
Footnotes
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Peharz et al. On Theoretical Properties of Sum-Product Networks. AISTATS (2015). ↩ ↩2
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Molina, Vergari et al. SPFLOW : An easy and extensible library for deep probabilistic learning using Sum-Product Networks. CoRR (2019). ↩ ↩2 ↩3 ↩4
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Poon and Domingos. Sum-Product Networks: A New Deep Architecture. UAI (2011). ↩
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Molina, Vergari et al. Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. AAAI (2018). ↩
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Di Mauro et al. Sum-Product Network structure learning by efficient product nodes discovery. AIxIA (2018). ↩
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Di Mauro, Gala et al. Random Probabilistic Circuits. UAI (2021). ↩
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Rahman et al. Cutset Networks: A Simple, Tractable, and Scalable Approach for Improving the Accuracy of Chow-Liu Trees. ECML-PKDD (2014). ↩
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Desana and Schnörr. Learning Arbitrary Sum-Product Network Leaves with Expectation-Maximization. CoRR (2016). ↩
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Peharz et al. Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. ICML (2020). ↩
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Peharz et al. Probabilistic Deep Learning using Random Sum-Product Networks. UAI (2020). ↩
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Van de Wolfshaar and Pronobis. Deep Generalized Convolutional Sum-Product Networks for Probabilistic Image Representations. PGM (2020). ↩
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Papamakarios et al. Masked Autoregressive Flow for Density Estimation. NeurIPS (2017). ↩
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Dinh et al. Density Estimation using RealNVP. ICLR (2017). ↩
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Dinh et al. NICE: Non-linear Independent Components Estimation. ICLR (2015). ↩