FLASH: Fast Bayesian Optimization for Data Analytic Pipelines
This work addresses the challenge of efficiently tuning complex data analytic pipelines for data scientists, though it appears incremental as it builds on existing Bayesian optimization techniques.
The paper tackles the problem of optimizing data analytic pipelines with high-dimensional and conditional search spaces by proposing FLASH, a two-layer Bayesian optimization method that selects algorithms and tunes hyperparameters efficiently. FLASH significantly outperforms previous state-of-the-art methods, achieving up to a 20% improvement in test error rate using only 50% of the time budget.
Modern data science relies on data analytic pipelines to organize interdependent computational steps. Such analytic pipelines often involve different algorithms across multiple steps, each with its own hyperparameters. To achieve the best performance, it is often critical to select optimal algorithms and to set appropriate hyperparameters, which requires large computational efforts. Bayesian optimization provides a principled way for searching optimal hyperparameters for a single algorithm. However, many challenges remain in solving pipeline optimization problems with high-dimensional and highly conditional search space. In this work, we propose Fast LineAr SearcH (FLASH), an efficient method for tuning analytic pipelines. FLASH is a two-layer Bayesian optimization framework, which firstly uses a parametric model to select promising algorithms, then computes a nonparametric model to fine-tune hyperparameters of the promising algorithms. FLASH also includes an effective caching algorithm which can further accelerate the search process. Extensive experiments on a number of benchmark datasets have demonstrated that FLASH significantly outperforms previous state-of-the-art methods in both search speed and accuracy. Using 50% of the time budget, FLASH achieves up to 20% improvement on test error rate compared to the baselines. FLASH also yields state-of-the-art performance on a real-world application for healthcare predictive modeling.