CtxPipe: Context-aware Data Preparation Pipeline Construction for Machine Learning

Machine learning models are only as good as their training data. Simple models trained on well-chosen features extracted from the raw data often outperform complex models trained directly on the raw data. Data preparation pipelines, which clean and derive features from the data, are therefore important for machine learning applications. However, constructing such pipelines is a resource-intensive process that involves deep human expertise. Our goal is to design an efficient framework for automatically finding high-quality data preparation pipelines. The main challenge is how to explore a large search space of pipeline components with the objective of computing features that maximize the performance of the downstream models. Existing solutions are limited in terms of feature quality, which results in low accuracies of the downstream models, while incurring significant runtime overhead. We present CtxPipe, a novel framework that addresses the limitations of previous works by leveraging contextual information to improve the pipeline construction process. Specifically, it uses pre-trained embedding models to capture the data semantics, which are then used to guide the selection of pipeline components. We implement CtxPipe with deep reinforcement learning and evaluate it against state-of-the-art automated pipeline construction solutions. Our comprehensive experiments demonstrate that CtxPipe outperforms all of the baselines in both model performance and runtime cost.