yoshitomo-matsubara/srsd-feynman_medium

---
pretty_name: SRSD-Feynman (Medium)
annotations_creators:
- expert
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- tabular-regression
task_ids: []
---

# Dataset Card for SRSD-Feynman (Medium set)

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:**
- **Repository:** https://github.com/omron-sinicx/srsd-benchmark
- **Paper:** [Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery](https://arxiv.org/abs/2206.10540)
- **Point of Contact:** [Yoshitaka Ushiku](mailto:[e-mail])

### Dataset Summary

Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
We carefully reviewed the properties of each formula and its variables in [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html) to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.

This is the ***Medium set*** of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:

[](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_medium/resolve/main/problem_table.pdf)

More details of these datasets are provided in [the paper and its supplementary material](https://openreview.net/forum?id=qrUdrXsiXX).

### Supported Tasks and Leaderboards

Symbolic Regression

## Dataset Structure

### Data Instances

Tabular data + Ground-truth equation per equation

Tabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.
Note that the number of variables (`num_variables`) varies from equation to equation.

Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.

### Data Fields

For each dataset, we have
1. train split (txt file, whitespace as a delimiter)
2. val split (txt file, whitespace as a delimiter)
3. test split (txt file, whitespace as a delimiter)
4. true equation (pickle file for sympy object)

### Data Splits

- train: 8,000 samples per equation
- val: 1,000 samples per equation
- test: 1,000 samples per equation

## Dataset Creation

### Curation Rationale

We chose target equations based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html).

### Annotations

#### Annotation process

We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.
First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.
Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.
In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.
Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.
Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly.
In addition, variables that take a specific sign were set to be sampled within that range.

#### Who are the annotators?

The main annotators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)

### Personal and Sensitive Information

N/A

## Considerations for Using the Data

### Social Impact of Dataset

We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.

### Discussion of Biases

Our choices of target equations are based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html), which are focused on a field of Physics.

### Other Known Limitations

Some variables used in our datasets indicate some numbers (counts), which should be treated as integer.
Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})

## Additional Information

### Dataset Curators

The main curators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)

### Licensing Information

Creative Commons Attribution 4.0

### Citation Information

[[OpenReview](https://openreview.net/forum?id=qrUdrXsiXX)] [[Video](https://www.youtube.com/watch?v=MmeOXuUUAW0)] [[Preprint](https://arxiv.org/abs/2206.10540)]
```bibtex
@article{matsubara2024rethinking,
title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery},
author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka},
journal={Journal of Data-centric Machine Learning Research},
year={2024},
url={https://openreview.net/forum?id=qrUdrXsiXX}
}
```

### Contributions

Authors:
- Yoshitomo Matsubara (@yoshitomo-matsubara)
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
- Yoshitaka Ushiku (@yushiku)

原始信息汇总

数据集概述

数据集名称

• 名称: SRSD-Feynman (Medium)

数据集创建者

• 标注创建者: 专家
• 语言创建者: 专家生成

语言和许可

• 语言: 英语
• 许可: CC-BY-4.0

多语言性和大小

• 多语言性: 单语种
• 大小: 100K<n<1M

源数据和任务

• 源数据: 扩展
• 任务类别: 表格回归

数据集详细信息

数据集描述

• 摘要: SRSD (Feynman) 数据集旨在讨论科学发现中符号回归的性能。数据集基于Feynman符号回归数据库，设计了合理的现实值采样范围，用于评估符号回归方法的潜力。
• 支持任务: 符号回归

数据集结构

• 数据实例: 表格数据 + 每条公式的真实方程
  • 表格数据: (num_samples, num_variables+1)，其中最后一列表示目标函数在给定变量下的输出。
  • 真实方程: 使用sympy表示的符号表示（方程）。
• 数据字段: 每个数据集包含训练、验证和测试分割的txt文件，以及真实方程的pickle文件。
• 数据分割:
  • 训练: 每条公式8,000样本
  • 验证: 每条公式1,000样本
  • 测试: 每条公式1,000样本

数据集创建

• 选择理由: 目标方程基于Feynman符号回归数据库。
• 标注过程: 对每个变量的采样范围进行了重大修订，确保与典型物理实验相对应。
• 标注者: Naoya Chiba (@nchiba), Ryo Igarashi (@rigarash)

使用数据集的考虑

• 社会影响: 数据集有助于研究符号回归在科学发现中的应用。
• 偏见讨论: 目标方程的选择基于物理学领域。
• 其他已知限制: 某些变量应视为整数，但由于32位整数的限制，某些变量被视为浮点数。

附加信息

• 数据集管理者: Naoya Chiba (@nchiba), Ryo Igarashi (@rigarash)
• 许可信息: 创意共享署名4.0
• 引用信息: 参见OpenReview