README.md

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license: cc-by-2.0
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# Dataset Card for Weaving Patterns of Size, \\(6 \times 5\\)

*Weaving patterns* are size \\(n \times (n−1)\\) matrices with \\(\{1, 2, \dots , n\}\\)-
entries introduced by \[1\] to study the number of reduced decompositions of the longest
permutation (which swaps \\(n\\) and \\(1\\), \\(n\\) - \\(1\\) and \\(2\\), etc.) up 
to commutation equivalence. The number
of such objects counts a wide range of combinatorial phenomena, including the number of parallel sorting
networks, the number of rhombic tilings of regular polygons, and is connected to 
the study of the higher Bruhat orders \[2\]. An \\(O(n^2)\\) algorithm for determining
if a given \\(\{1, 2, . . . , n\}\\)-matrix is a valid weaving pattern
exists but gives no additional insight into the structure of
weaving patterns and correspondingly the asymptotics of
reduced decompositions. The enumeration of reduced decompositions 
up to commutation equivalence has been studied by many including
Knuth \[3\] and Stanley \[4\]. An exact formula is likely out of reach,
so asymptotic upper and lower bounds are of great interest.
ML models that can detect necessary or sufficient conditions
for a matrix to be a valid weaving pattern have the potential
to lead to substantial improvements in the upper bound.

This dataset is a mixture of enriched weaving patterns and
non-weaving pattern matrices with \\(\{1, 2, \dots, 6\}\\)-entries.

## Updates

- 2026-01-15: We updated both the train and test files to eliminate an error where weaving
  patterns and non-weaving patterns had slightly different representations. This difference led to artificially high accuracy in models
  who learned this short cut rather than mathematically meaningful differences.

## Dataset Details

Each matrix is stored on a single line in row-major format. For instance,

`[ 5, 4, 3, 2, 1, 6, 2, 3, 4, 1, 6, 2, 3, 5, 1, 6, 2, 4, 5, 1, 6, 3, 4, 5, 1, 6, 5, 4, 3, 2 ]`.

Labels are `1` (not a weaving pattern) and `0` (a weaving pattern).

**Statistics**
|  | Weaving patterns | Non-weaving patterns | Total instances |
|----------|----------|---------------|--------|
| Train | 634 | 1,116 | 1,750 |
| Test  | 275 | 467 | 742 |

This dataset is small, we encourage users to also look at our dataset of weaving patterns of size \\(7 \times 6\\).

**Math question:** Find necessary or sufficient conditions to distinguish between 
weaving pattern matrices and non-weaving pattern matrices. These should be more efficient than the 
\\(O(n^2)\\) algorithm that can be found in the references above.

**ML task:** Train a model to classify whether a \\(\{1, 2, . . . , 6\}\\)-
matrix is a weaving pattern or not. This task is framed as
binary classification. Extract mathematical insights from a performant model.

## Small model performance

We provide some basic baselines for this task. Benchmarking details can be found in the associated paper.

| Size | Logistic regression | MLP | Transformer | Guessing largest class | 
|----------|----------|-----------|------------|------------|
| \\(6 \times 5\\) | \\(70.4\%\\) | \\(86.1 \% \pm 0.2\%\\) | \\(85.9\% \pm 2.3\%\\)| \\(63.3\%\\) |

The \\(\pm\\) signs indicate 95% confidence intervals from random weight initialization and training.

## Further information

- **Curated by:** Herman Chau
- **Funded by:** Pacific Northwest National Laboratory
- **Language(s) (NLP):** NA
- **License:** CC-by-2.0

### Dataset Sources

Data generation scripts can be found [here](https://github.com/pnnl/ML4AlgComb/tree/master/weaving_patterns).

- **Repository:** [ACD Repo](https://github.com/pnnl/ML4AlgComb/tree/master/weaving_patterns)
  
## Citation

**BibTeX:**


    @article{chau2025machine,
        title={Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics},
        author={Chau, Herman and Jenne, Helen and Brown, Davis and He, Jesse and Raugas, Mark and Billey, Sara and Kvinge, Henry},
        journal={arXiv preprint arXiv:2503.06366},
        year={2025}
    }


**APA:**

Chau, H., Jenne, H., Brown, D., He, J., Raugas, M., Billey, S., & Kvinge, H. (2025). Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv:2503.06366.

## Dataset Card Contact

Henry Kvinge, acdbenchdataset@gmail.com

## References

\[1\] Felsner, Stefan. "On the number of arrangements of pseudolines." Proceedings of the twelfth annual Symposium on Computational Geometry. 1996.  
\[2\] Chau, Herman. "On enumerating higher bruhat orders through deletion and contraction." arXiv preprint arXiv:2412.10532 (2024).
\[3\] Knuth, Donald E., ed. Axioms and hulls. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992.
\[4\] Stanley, Richard P. "On the number of reduced decompositions of elements of Coxeter groups." European Journal of Combinatorics 5.4 (1984): 359-372.