TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
It addresses the need for scalable and efficient error detection in machine learning for practitioners, though it is incremental as it builds on existing relational and functional programming concepts.
The paper tackles the problem of finding errors in machine learning applications by introducing TorchQL, a programming framework for specifying and checking integrity constraints, which enables up to 13x faster query executions and up to 40% shorter queries than baselines.
Finding errors in machine learning applications requires a thorough exploration of their behavior over data. Existing approaches used by practitioners are often ad-hoc and lack the abstractions needed to scale this process. We present TorchQL, a programming framework to evaluate and improve the correctness of machine learning applications. TorchQL allows users to write queries to specify and check integrity constraints over machine learning models and datasets. It seamlessly integrates relational algebra with functional programming to allow for highly expressive queries using only eight intuitive operators. We evaluate TorchQL on diverse use-cases including finding critical temporal inconsistencies in objects detected across video frames in autonomous driving, finding data imputation errors in time-series medical records, finding data labeling errors in real-world images, and evaluating biases and constraining outputs of language models. Our experiments show that TorchQL enables up to 13x faster query executions than baselines like Pandas and MongoDB, and up to 40% shorter queries than native Python. We also conduct a user study and find that TorchQL is natural enough for developers familiar with Python to specify complex integrity constraints.