Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning
Ori Bar El, Tova Milo, Amit Somech
Abstract
Exploratory Data Analysis (EDA) is an essential yet highly demanding task. To get a head start before exploring a new dataset, data scientists often prefer to view existing EDA notebooks -- illustrative, curated exploratory sessions, on the same dataset, that were created by fellow data scientists who shared them online. Unfortunately, such notebooks are not always available (e.g., if the dataset is new or confidential). To address this, we present ATENA, a system that takes an input dataset and auto-generates a compelling exploratory session, presented in an EDA notebook. We shape EDA into a control problem, and devise a novel Deep Reinforcement Learning (DRL) architecture to effectively optimize the notebook generation. Though ATENA uses a limited set of EDA operations, our experiments show that it generates useful EDA notebooks, allowing users to gain actual insights.