tpcp: Tiny Pipelines for Complex Problems - A set of framework independent helpers for algorithms development and evaluation

Küderle A, Richer R, Simpetru R, Eskofier B (2023)

Publication Type: Journal article, Original article

Publication year: 2023

Journal

Journal of Open Source Software JOSS

Book Volume: 8

Pages Range: 4953

Issue: 82

Journal Issue: 82

URI: https://joss.theoj.org/papers/10.21105/joss.04953

DOI: 10.21105/joss.04953

Open Access Link: https://joss.theoj.org/papers/10.21105/joss.04953

Abstract

During algorithm development and analysis researchers regularly use software libraries developed for their specific domain. With such libraries, complex analysis tasks can often be reduced to a couple of lines of code. This not only reduces the amount of implementation required but also prevents errors.

The best developer experience is usually achieved when the entire analysis can be represented with the tools provided by a single library. For example, when an entire machine learning pipeline is represented by a scikit-learn pipeline (Pedregosa et al., 2018), it is extremely easy to switch out and train algorithms. Furthermore, train/test leaks and other methodological errors at various stages in the analysis are automatically prevented – even if the user might not be aware of these issues.

However, if the performed analysis gets too complex, too specific to an application domain, or requires the use of tooling and algorithms from multiple frameworks, developers lose a lot of the benefits provided by individual libraries. In turn, the required skill level and the chance of methodological errors rise.

With tpcp we attempt to overcome the issue by providing higher-level tooling and structure for algorithm development and evaluation that is independent of the frameworks required for the algorithm implementation.

Authors with CRIS profile

Arne Küderle Machine Learning and Data Analytics Lab Robert Richer Machine Learning and Data Analytics Lab Raul Simpetru Juniorprofessur für Neuromuscular Physiology and Neural Interfacing Björn Eskofier Machine Learning and Data Analytics Lab

Additional Organisation(s)

Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung

Related research project(s)

Berührungslose Messung von Stress, seiner Determinanten und Konsequenzen (SFB 1483 EmpkinS D03) Empatho-Kinaesthetic Sensor Technology (SFB 1483 EmpkinS) July 1, 2021 - June 30, 2025 BioPsyKit – An Open-Source Python Package for the Analysis of Biopsychological Data (BioPsyKit) Sept. 1, 2021 - Sept. 1, 2024 Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement (MOBILISE-D) April 1, 2019 - March 31, 2024

How to cite

APA:

Küderle, A., Richer, R., Simpetru, R., & Eskofier, B. (2023). tpcp: Tiny Pipelines for Complex Problems - A set of framework independent helpers for algorithms development and evaluation. Journal of Open Source Software, 8(82), 4953. https://doi.org/10.21105/joss.04953

MLA:

Küderle, Arne, et al. "tpcp: Tiny Pipelines for Complex Problems - A set of framework independent helpers for algorithms development and evaluation." Journal of Open Source Software 8.82 (2023): 4953.

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