Wydajność narzędzi uczenia maszynowego. Analiza porównawcza bibliotek w interpretowanych i kompilowanych językach programowania

• Autorzy: Wiejak Tomasz , Smołka Jakub

Identyfikatory

DOI

10.35784/jcsi.6589

Warianty tytułu

EN

Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages

• Języki publikacji: PL

Abstrakty

PL

W artykule porównano narzędzia uczenia maszynowego na przykładzie kilku popularnych języków programowania. Przetestowano i porównano istniejące narzędzia w następujących językach programowania: Python, Java, R, Julia, C#. Na potrzeby artykułu w każdym badanym języku zostały stworzone algorytmy operujące na tym samym zbiorze testowym i wykorzystujące algorytmy z tej samej grupy. Rejestrowane wartości obejmowały czas działania programu, liczbę linii kodu, jak i dokładność uczenia modeli. Na podstawie wszystkich otrzymanych danych wyciągnięto wnioski, a w rezultacie stwierdzono, że biblioteki języków interpretowanych pod względem tworzenia rozwiązań uczenia maszynowego są skuteczniejsze niż biblioteki języków kompilowanych.

EN

The article compares machine learning tools using the example of several popular programming languages. Existing tools in the following programming languages were tested and compared with each other: Python, Java, R, Julia, C#. For the needs of article, algorithms were created in each studied language, operating on the same test set and using algorithms from the same group. The collected results included the program's running time, number of lines of code and accuracy of trained model. Based on the obtained data, conclusions were drawn that interpreted language libraries in terms of creating machine learning solutions are more effective than compiled language libraries.

Słowa kluczowe

PL

uczenie maszynowe; język interpretowany; język kompilowany

EN

machine learning; interpreted language; compiled language

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Journal of Computer Sciences Institute
• Rocznik: 2024
• Tom: Vol. 33
• Strony: 339--345
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Wiejak Tomasz

• Lublin University of Technology (Poland)

autor

Smołka Jakub

• Lublin University of Technology (Poland)

• https://orcid.org/0000-0002-8350-2537

Bibliografia

• [1] B. Johnson, A. S. Chandran, Comparison between Python, Java and R programming language in machine learning, International Research Journal of Modernization in Engineering Technology and Science 3(6) (2021) 1–6.
• [2] M. Wickham, Practical Java Machine Learning, Apress, Irving, 2018.
• [3] I. H. Witten, E. Frank, L. E. Trigg, M. A. Hall, G. Holmes, S. J. Cunningham, Weka: Practical machine learning tools and techniques with Java implementations, Working Paper, The University of Waikato, Hamilton, 1999.
• [4] T. Abeel, Y. Van de Peer, Y. Saeys, Java-ML: A Machine Learning Library, Journal of Machine Learning Research 10(34) (2009) 931–934, https://dl.acm.org/doi/10.5555/1577069.1577103.
• [5] J. Heaton, Encog: Library of Interchangeable Machine Learning Models for Java and C#, Journal of Machine Learning Research 16(36) (2015) 1243–1247, https://doi.org/10.48550/arXiv.1506.04776.
• [6] L. I. Hatledal, F. Sanfilippo, H. Zhang, JIOP: A Java Intelligent Optimisation and Machine Learning Framework, Proceedings of the European Conference on Modelling and Simulation (2014) 1-7, http://dx.doi.org/10.7148/2014-0101.
• [7] C. Rackauckas, R. Anantharaman, A. Edelman, S. Gowda, M. Gwozdz, A. Jain, C. Laughman, Y. Ma, F. Martinuzzi, A. Pal, U. Rajput, E. Saba, V. B. Shah, Composing Modeling And Simulation With Machine Learning In Julia, Proceedings of the Annual Modeling and Simulation Conference (ANNSIM) (2022) 1–17, https://doi.org/10.48550/arXiv.2105.05946.
• [8] K. Gao, G. Mei, F. Piccialli, S. Cuomo, J. Tu, Z. Huo, Julia language in machine learning: Algorithms, applications, and open issues, Computer Science Review 37 (2020) 1- 13, https://doi.org/10.1016/j.cosrev.2020.100254.
• [9] A. D. Blaom, F. Kiraly, T. Lienart, Y. Simillides, D. Arenas, S. J. Vollmer, MLJ: A Julia package for composable Machine Learning, Journal of Open Source Software 5(55) (2020) 1-9, https://doi.org/10.21105/joss.02704.
• [10] M. Innes, Flux: Elegant machine learning with Julia, Journal of Open Source Software 3(25) (2018) 1, https://doi.org/10.21105/joss.00602.
• [11] D. Yuret, Knet: beginning deep learning with 100 lines of Julia, Proceedings of the Machine Learning Systems Workshop at NIPS (2016) 1-7.
• [12] H-A. Goh, C-K. Ho, F. S. Abas, Front-end deep learning web apps development and deployment: a review, Applied Intelligence 53(12) (2023) 15923–15945, http://dx.doi.org/10.1007/s10489-022-04278-6.
• [13] C. Molnar, G. Casalicchio, B. Bischl, iml: An R package for Interpretable Machine Learning, Journal of Open Source Software 3(26) (2018) 1-2, https://doi.org/10.21105/joss.00786.
• [14] M. Lang, M. Binder, J. Richter, P. Schratz, F. Pfisterer, S. Coors, Q. Au, G. Casalicchio, L. Kotthoff, B. Bischl, mlr3: A modern object-oriented machine learning framework in R, Journal of Open Source Software 4(44) (2019) 1-3, https://doi.org/10.21105/joss.01903.
• [15] B. Bischl, M. Lang, L. Kotthoff, J. Schiffner, J. Richter, E. Studerus, G. Casalicchio, Z. M. Jones, mlr: Machine Learning in R, Journal of Machine Learning Research 17(170) (2016) 1–5.
• [16] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research 12(85) (2012) 2825–2830, https://doi.org/10.48550/arXiv.1201.0490.
• [17] S. Ali, S. Qayyum, A Pragmatic Comparison of Four Different Programming Languages, Preprints (2021) 1-14, https://doi.org/10.14293/s2199-1006.1.sor-.pp5rv1o.v1.
• [18] F. Zehra, M. Javed, D. Khan, M. Pasha, Comparative Analysis of C++ and Python in Terms of Memory and Time, Preprints (2020) 1-11, http://dx.doi.org/10.20944/preprints202012.0516.v1.
• [19] M. Ramchandani, H. Khandere, P. Singh, P. Rajak, N. Suryawanshi, A. S. Jangde, L. Arya, P. Kumar, M. Sahu, Survey: Tensorflow in Machine Learning, Journal of Physics: Conference Series 2273(1) (2022) 1-12, http://dx.doi.org/10.1088/1742-6596/2273/1/012008.
• [20] M. N. Gevorkyan, A. V. Demidova, T. S. Demidova, A. A. Sobolev, Review and comparative analysis of machine learning libraries for machine learning, Discrete and Continuous Models and Applied Computational Science
• 27(4) (2019) 305–315, http://dx.doi.org/10.22363/2658- 4670-2019-27-4-305-315.
• [21] K. Hornik, C. Buchta, A. Zeileis, Open-source machine learning: R Meets Weka, Computational Statistics 24(2) (2009) 225–232, http://dx.doi.org/10.1007/s00180-008- 0119-7.
• [22] M. Innes, S. Karpinski, V. B. Shah, D. Barber, P. Stenetorp, T. Besard, J. Bradbury, V. Churavy, S. Danisch, A. Edelman, J. Malmaud, J. Revels, D. Yuret, On Machine Learning and Programming Languages, Proceedings of the SysML Conference (2018) 1-3.
• [23] Ž. Ð. Vujović, Classification Model Evaluation Metrics, International Journal of Advanced Computer Science and Applications 12(6) (2021) 599–606, https://dx.doi.org/10.14569/IJACSA.2021.0120670.
• [24] M. Hossin, M. N. Sulaiman, A review on evaluation metrics for data classification evaluations, International Journal of Data Mining & Knowledge Management Process 5(2) (2015) 1–11, http://dx.doi.org/10.5121/ijdkp.2015.5201.
• [25] K. R. Shahapure, C. Nicholas, Cluster Quality Analysis Using Silhouette Score, Proceedings of the 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (2020) 747–748, http://dx.doi.org/10.1109/DSAA49011.2020.00096.
• [26] Data set of cars and their parameters, https://archive.ics.uci.edu/dataset/9/auto+mpg, [15.07.2024].
• [27] Data set of possible diabetes in patients, https://www.kaggle.com/datasets/uciml/pima-indiansdiabetes- database, [15.07.2024].
• [28] Data set of shopping center customers, https://www.kaggle.com/datasets/vjchoudhary7/customer -segmentation-tutorial-in-python, [15.07.2024].

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).