TLSQL: Table Learning Structured Query Language

TL;DR

TLSQL is a Python library that simplifies integrating table learning with relational databases by enabling SQL-based specifications, reducing data preparation effort and making machine learning more accessible for database practitioners.

Contribution

TLSQL introduces a declarative, SQL-like approach for table learning directly over relational databases, eliminating the need for explicit data export and extensive feature engineering.

Findings

Effective integration of table learning with databases demonstrated.

Reduces data preparation time for machine learning tasks.

Facilitates easier adoption of machine learning in database workflows.

Abstract

Table learning, which lies at the intersection of machine learning and modern database systems, has recently attracted growing attention. However, existing table learning frameworks typically require explicit data export and extensive feature engineering, creating a high barrier for database practitioners. We present TLSQL (Table Learning Structured Query Language), a system that enables table learning directly over relational databases via SQL-like declarative specifications. TLSQL is implemented as a lightweight Python library that translates these specifications into standard SQL queries and structured learning task descriptions. The generated SQL queries are executed natively by the database engine, while the task descriptions are consumed by downstream table learning frameworks. This design allows users to focus on modeling and analysis rather than low-level data preparation and pipeline orchestration. Experiments on real-world datasets demonstrate that TLSQL effectively lowers the barrier to integrating machine learning into database-centric workflows. Our code is available at https://github.com/rllm-project/tlsql/.