Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
pgmpy: A Python Toolkit for Bayesian Networks
Authors: Ankur Ankan, Johannes Textor
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to extensibility, pgmpy ensures scalability of the implemented algorithms by benchmarking runtime and memory usage (Ankan, 2023), and reliability by extensive unit testing and validation against other open-source packages. |
| Researcher Affiliation | Academia | Ankur Ankan EMAIL Johannes Textor EMAIL Institute of Computing and Information Sciences, Radboud University, Nijmegen, Netherlands |
| Pseudocode | No | The paper describes algorithms and workflows but does not present any pseudocode or algorithm blocks. |
| Open Source Code | Yes | pgmpy is released under the MIT License; the source code is available at: https://github.com/pgmpy/pgmpy |
| Open Datasets | No | The paper discusses the capabilities of the pgmpy library for tasks like structure learning from data but does not provide specific access information (links, DOIs, citations with authors/years) for any datasets used in its own context or evaluation. |
| Dataset Splits | No | The paper describes a software toolkit and its features. It does not conduct experiments on a specific dataset that would require reporting dataset splits (e.g., train/test/validation). |
| Hardware Specification | No | The paper mentions benchmarking runtime and memory usage, referencing an external benchmark document (Ankan, 2023), but it does not specify any hardware details (e.g., GPU/CPU models, memory amounts) used for its own development or testing within this paper. |
| Software Dependencies | No | The paper describes pgmpy, a Python package, and mentions other packages for comparison (e.g., pomegranate, pyBNesian, bayespy, DoWhy, dagitty, bnlearn, pcalg, libDAI). However, it does not provide specific version numbers for these or other ancillary software components used for development or evaluation. |
| Experiment Setup | No | The paper describes a software toolkit and its algorithms but does not present specific experimental setup details such as hyperparameters, training configurations, or system-level settings, as it is not an empirical study involving model training. |