Learning Governing Abstractions Behind Integer Sequences
Integer sequences are of central importance to the modeling of concepts admitting complete finitary descriptions. We introduce a novel view on the learning of such concepts and lay down a set of benchmarking tasks aimed at conceptual understanding by machine learning models. These tasks indirectly assess model ability to abstract, and challenge them to reason both interpolatively and extrapolatively from the knowledge gained by observing representative examples. To further aid research in knowledge representation and reasoning, we present FACT, the Finitary Abstraction Comprehension Toolkit. The toolkit surrounds a large dataset of integer sequences comprising both organic and synthetic entries, a library for data pre-processing and generation, a set of model performance evaluation tools, and a collection of baseline model implementations, enabling the making of the future advancements with ease.
This repository consists of the OEIS processing code, FACTLIB, and the Benchmarking Setup. Each is described below in more detail.
The main FACT dataset is available as a SQLite database on the link below. You can use any SQLite library to make the connection from your code to the dataset.
https://polybox.ethz.ch/index.php/s/XcUS8GttrM75HyK
To quickly inspect the dataset, tools such as DB Browser (SQLite) might come handy.
There is a UNIQUE INDEX on the oeis_id column to speed up listing and ID lookups. If you plan to search the dataset through a different column, consider indexing it to improve performance.
Additionally, there is also a sub-dataset created specifically for our benchmark that contains no NLP annotations or other additional information that is solicited for OEIS entries.
This file is significantly smaller and contains: the oeis_ids uniquely identifying each sequence, the sequence itself, and some flag (1 or 0) fields marking whether the sequence is trigonometric, polynomial, exponential, etc..
https://polybox.ethz.ch/index.php/s/sV5v8zeDZm6ArTd
More information can be found in the README distributed with the version of the dataset.
You will find a QuickStart Jupyter Notebook file under ./QuickStart. pytorch and torchvision are its two notable requirements.
You will find all code and more information about the processing steps for OEIS data that was included into the FACT dataset under ./OEIS Processing.
We recommend starting with the README.md file in that directory.
A library for generation of synthetic sequences, available under ./FACTLIB.
Individual generation sub-modules and functions, main ones documented with docstrings, can then be invoked directly or used as ready-made snippets as in main.py.
A benchmarking setup with a range of models and tasks as well as apropriate evaluation metrics.
Feel free to contact [email protected] and [email protected] in case you have any questions.
- Peter Belcak (maintainer)
- Emanual Jampen (OEIS data processing)
- Ard Kastrati (maintainer, OEIS data processing, synthetic sequence generation)
- Flavio Schenker (baseline model implementation)
- Neil Sloane (the author and maintainer of The Online Encyclopedia of Integer Sequences https://oeis.org/)
This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/3.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/3.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.