Abstract
A large part of the time invested in data science is spent on manual preparation of data. Transforming wrongly formatted columns into useful features takes up a significant part of this time. We present the avatar algorithm for automatically learning programs that perform this type of feature wrangling. Instead of relying on users to guide the wrangling process, avatar directly uses the predictive performance of machine learning models to measure its progress during wrangling. We use datasets from Kaggle to show that avatar improves raw data for prediction, and square it off against human data scientists.
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References
Bavishi, R., Lemieux, C., Fox, R., Sen, K., Stoica, I.: Autopandas: neural-backed generators for program synthesis. In: Proceedings of the ACM on Programming Languages 3(OOPSLA), pp. 1–27 (2019)
Dasu, T., Johnson, T.: Exploratory Data Mining and Data Cleaning, vol. 479. Wiley, New York (2003)
Drosos, I., Barik, T., Guo, P.J., DeLine, R., Gulwani, S.: Wrex: a unified programming-by-example interaction for synthesizing readable code for data scientists. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2020)
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J.T., Blum, M., Hutter, F.: Auto-sklearn: efficient and robust automated machine learning. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 113–134. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5_6
He, Y., Chu, X., Ganjam, K., Zheng, Y., Narasayya, V., Chaudhuri, S.: Transform-data-by-example (TDE) an extensible search engine for data transformations. Proc. VLDB Endow. 11(10), 1165–1177 (2018)
Jin, Z., Anderson, M.R., Cafarella, M., Jagadish, H.: Foofah: transforming data by example. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 683–698 (2017)
Kandel, S., Paepcke, A., Hellerstein, J., Heer, J.: Wrangler: interactive visual specification of data transformation scripts. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3363–3372 (2011)
Kanter, J.M., Veeramachaneni, K.: Deep feature synthesis: towards automating data science endeavors. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10. IEEE (2015)
Kaul, A., Maheshwary, S., Pudi, V.: Autolearn–automated feature generation and selection. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 217–226. IEEE (2017)
Le, V., Gulwani, S.: Flashextract: a framework for data extraction by examples. In: Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 542–553 (2014)
Lundberg, S.M., et al.: From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2(1), 2522–5839 (2020)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, pp. 4765–4774 (2017)
Olson, R.S., Moore, J.H.: TPOt: a tree-based pipeline optimization tool for automating machine learning. In: Workshop on Automatic Machine Learning, pp. 66–74. PMLR (2016)
Raman, V., Hellerstein, J.M.: Potter’s wheel: an interactive data cleaning system. VLDB 1, 381–390 (2001)
Shapley, L.S.: A value for n-person games. Contrib. Theor. Games 2(28), 307–317 (1953)
Van Wolputte, E., Korneva, E., Blockeel, H.: Mercs: multi-directional ensembles of regression and classification trees. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 4276–4283. AAAI Publications, New Orleans, Louisiana, USA (2018)
Yan, C., He, Y.: Auto-suggest: learning-to-recommend data preparation steps using data science notebooks. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 1539–1554 (2020)
Yang, C., Akimoto, Y., Kim, D.W., Udell, M.: Oboe: collaborative filtering for autoML model selection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1173–1183 (2019)
Acknowledgements
This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No [694980] SYNTH: Synthesising Inductive Data Models). This research received funding from the Flemish Government (AI Research Program). Sebastijan Dumancic is funded by the Research Foundation-Flanders (FWO).
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Verbruggen, G., Van Wolputte, E., Dumančić, S., De Raedt, L. (2021). avatar—Automated Feature Wrangling for Machine Learning. In: Abreu, P.H., Rodrigues, P.P., Fernández, A., Gama, J. (eds) Advances in Intelligent Data Analysis XIX. IDA 2021. Lecture Notes in Computer Science(), vol 12695. Springer, Cham. https://doi.org/10.1007/978-3-030-74251-5_19
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