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Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning (2402.15997v2)

Published 25 Feb 2024 in cs.HC, cs.GR, and cs.LG

Abstract: Quality colormaps can help communicate important data patterns. However, finding an aesthetically pleasing colormap that looks "just right" for a given scenario requires significant design and technical expertise. We introduce Cieran, a tool that allows any data analyst to rapidly find quality colormaps while designing charts within Jupyter Notebooks. Our system employs an active preference learning paradigm to rank expert-designed colormaps and create new ones from pairwise comparisons, allowing analysts who are novices in color design to tailor colormaps to their data context. We accomplish this by treating colormap design as a path planning problem through the CIELAB colorspace with a context-specific reward model. In an evaluation with twelve scientists, we found that Cieran effectively modeled user preferences to rank colormaps and leveraged this model to create new quality designs. Our work shows the potential of active preference learning for supporting efficient visualization design optimization.

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References (79)
  1. Pieter Abbeel and Andrew Y Ng. 2004. Apprenticeship Learning via Inverse Reinforcement Learning. In Proceedings of the Twenty-First International Conference on Machine Learning. 1.
  2. When red means good, bad, or Canada: exploring people’s reasoning for choosing color palettes. In 2021 IEEE Visualization Conference (VIS). IEEE, 56–60.
  3. April: Active preference learning-based reinforcement learning. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II 23. Springer, 116–131.
  4. Affective color in visualization. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 1364–1374.
  5. A rule-based tool for assisting colormap selection. In Proceedings Visualization’95. IEEE, 118–125.
  6. Active Preference Learning with Discrete Choice Data. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS’07). Curran Associates Inc., Red Hook, NY, USA, 409–416.
  7. Molly Brown. 2021. Tableau Research leader and resident color expert Maureen Stone talks R&D at Tableau. https://www.tableau.com/blog/how-maureen-stone-makes-data-more-accessible-tableau-research Accessed: 02-06-2023.
  8. The Good, the Bad, and the Ugly: A Theoretical Framework for the Assessment of Continuous Colormaps. IEEE Transactions on Visualization and Computer Graphics 24, 1 (jan 2018), 923–933. https://doi.org/10.1109/TVCG.2017.2743978
  9. Asking Easy Questions: A User-Friendly Approach to Active Reward Learning. In Proceedings of the 3rd Conference on Robot Learning. arXiv:1910.04365 http://arxiv.org/abs/1910.04365
  10. APReL: A Library for Active Preference-based Reward Learning Algorithms. In ACM/IEEE International Conference on Human-Robot Interaction, Vol. 2022-March. 613–617. https://doi.org/10.1109/HRI53351.2022.9889650 arXiv:2108.07259
  11. Vizlinter: A linter and fixer framework for data visualization. IEEE Transactions on Visualization and Computer Graphics 28, 1 (2021), 206–216.
  12. Preference-based policy iteration: Leveraging preference learning for reinforcement learning. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I 11. Springer, 312–327.
  13. The effect of color scales on climate scientists’ objective and subjective performance in spatial data analysis tasks. IEEE Transactions on Visualization and Computer Graphics 26, 3 (2018), 1577–1591.
  14. Carl de Boor. 1972. On calculating with B-splines. Journal of Approximation Theory 6, 1 (jul 1972), 50–62. https://doi.org/10.1016/0021-9045(72)90080-9
  15. Convergence of Optimistic and Incremental Q-Learning. In Advances in Neural Information Processing Systems, T Dietterich, S Becker, and Z Ghahramani (Eds.), Vol. 14. MIT Press. https://proceedings.neurips.cc/paper_files/paper/2001/file/6f2688a5fce7d48c8d19762b88c32c3b-Paper.pdf
  16. Roland Geraerts and Mark H. Overmars. 2004. A Comparative Study of Probabilistic Roadmap Planners. In Algorithmic Foundations of Robotics V. Springer, 43–57. https://doi.org/10.1007/978-3-540-45058-0_4
  17. Colorgorical: Creating discriminable and preferable color palettes for information visualization. IEEE Transactions on Visualization and Computer Graphics 23, 1 (jan 2017), 521–530. https://doi.org/10.1109/TVCG.2016.2598918
  18. D. A. Green. 2011. A colour scheme for the display of astronomical intensity images. Bulletin of the Astronomical Society of India 39, 2 (aug 2011), 289–295. arXiv:1108.5083 http://arxiv.org/abs/1108.5083
  19. Exploring network structure, dynamics, and function using NetworkX. Technical Report. Los Alamos National Lab.(LANL), Los Alamos, NM (United States).
  20. J. H. Halton. 1960. On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numer. Math. 2, 1 (dec 1960), 84–90. https://doi.org/10.1007/BF01386213
  21. Array programming with NumPy. Nature 585, 7825 (Sept. 2020), 357–362. https://doi.org/10.1038/s41586-020-2649-2
  22. Mark Harrower and Cynthia A. Brewer. 2003. ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps. The Cartographic Journal 40, 1 (2003), 27–37. https://doi.org/10.1179/000870403235002042 arXiv:https://www.tandfonline.com/doi/pdf/10.1179/000870403235002042
  23. BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations. IEEE Transactions on Visualization and Computer Graphics 29, 1 (2022), 363–373.
  24. Visual perception and mixed-initiative interaction for assisted visualization design. IEEE Transactions on Visualization and Computer Graphics 14, 2 (2008), 396–411.
  25. Jeffrey Heer. 2019. Agency plus automation: Designing artificial intelligence into interactive systems. Proceedings of the National Academy of Sciences 116, 6 (2019), 1844–1850. https://doi.org/10.1073/pnas.1807184115 arXiv:https://www.pnas.org/doi/pdf/10.1073/pnas.1807184115
  26. Visualint: Sketchy in situ annotations of chart construction errors. In Computer Graphics Forum, Vol. 39. Wiley Online Library, 219–228.
  27. J. D. Hunter. 2007. Matplotlib: A 2D graphics environment. Computing in Science & Engineering 9, 3 (2007), 90–95. https://doi.org/10.1109/MCSE.2007.55
  28. On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. Neural Computation 6, 6 (11 1994), 1185–1201. https://doi.org/10.1162/neco.1994.6.6.1185 arXiv:https://direct.mit.edu/neco/article-pdf/6/6/1185/812885/neco.1994.6.6.1185.pdf
  29. Jupyter Widgets Team. 2023. ipywidgets: Interactive Widgets for the Jupyter Notebook. https://github.com/jupyter-widgets/ipywidgets GitHub repository.
  30. Daniel Kahneman and Amos Tversky. 1979. Prospect Theory: An Analysis of Decision under Risk. Econometrica 47, 2 (mar 1979), 263. https://doi.org/10.2307/1914185
  31. Learning an Urban Air Mobility Encounter Model from Expert Preferences. In AIAA/IEEE Digital Avionics Systems Conference - Proceedings, Vol. 2019-Septe. https://doi.org/10.1109/DASC43569.2019.9081648 arXiv:1907.05575
  32. W Bradley Knox and Peter Stone. 2009. Interactively shaping agents via human reinforcement: The TAMER framework. In Proceedings of the Fifth International Conference on Knowledge Capture. 9–16.
  33. Yuki Koyama and Masataka Goto. 2022. BO as Assistant: Using Bayesian Optimization for Asynchronously Generating Design Suggestions. UIST 2022 - Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (2022). https://doi.org/10.1145/3526113.3545664
  34. Sequential line search for efficient visual design optimization by crowds. ACM Transactions on Graphics 36, 4 (aug 2017), 1–11. https://doi.org/10.1145/3072959.3073598
  35. K. S. Krishnan. 1977. Incorporating Thresholds of Indifference in Probabilistic Choice Models. Management Science 23, 11 (1977), 1224–1233. http://www.jstor.org/stable/2630661
  36. Andrea Lau and Andrew Vande Moere. 2007. Towards a model of information aesthetics in information visualization. In 2007 11th International Conference Information Visualization (IV’07). IEEE, 87–92.
  37. Image-Driven Harmonious Color Palette Generation for Diverse Information Visualization. IEEE Transactions on Visualization and Computer Graphics (2022), 1–16. https://doi.org/10.1109/TVCG.2022.3226218
  38. Yang Liu and Jeffrey Heer. 2018. Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3173574.3174172
  39. S. Lloyd. 1982. Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 2 (mar 1982), 129–137. https://doi.org/10.1109/TIT.1982.1056489
  40. R.Duncan Luce. 1977. The Choice Axiom After Twenty Years. Journal of Mathematical Psychology 15, 3 (jun 1977), 215–233. https://doi.org/10.1016/0022-2496(77)90032-3
  41. The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research and Application 26, 5 (oct 2001), 340–350. https://doi.org/10.1002/col.1049
  42. Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics 13, 6 (2007), 1137–1144.
  43. Joel Max. 1960. Quantizing for minimum distortion. IEEE Transactions on Information Theory 6, 1 (mar 1960), 7–12. https://doi.org/10.1109/TIT.1960.1057548
  44. Andrew McNutt and Gordon Kindlmann. 2018. Linting for visualization: Towards a practical automated visualization guidance system. In VisGuides: 2nd Workshop on the Creation, Curation, Critique and Conditioning of Principles and Guidelines in Visualization. 1–14.
  45. Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 21, 6 (jun 1953), 1087–1092. https://doi.org/10.1063/1.1699114
  46. ColorCAT: Guided Design of Colormaps for Combined Analysis Tasks. Eurographics Conference on Visualization, EuroVis 2015 - Short Papers (2015), 115–119. https://doi.org/10.2312/eurovisshort.20151135
  47. Wojciech Mokrzycki and Maciej Tatol. 2011. Color difference Delta E - A survey. Machine Graphics and Vision 20 (04 2011), 383–411.
  48. Kenneth Moreland. 2009. Diverging Color Maps for Scientific Visualization. In Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II (Las Vegas, Nevada) (ISVC ’09). Springer-Verlag, Berlin, Heidelberg, 92–103. https://doi.org/10.1007/978-3-642-10520-3_9
  49. Kenneth D Moreland. 2015. Why We Use Bad Color Maps and What You Can Do About It. Technical Report. Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
  50. Formalizing visualization design knowledge as constraints: Actionable and extensible models in draco. IEEE Transactions on Visualization and Computer Graphics 25, 1 (2018), 438–448.
  51. Isaac Muse. 2023. coloraide: A Python library for color. https://github.com/facelessuser/coloraide GitHub repository.
  52. Automatic Improvement of Continuous Colormaps in Euclidean Colorspaces. Computer Graphics Forum 40, 3 (2021), 361–373. https://doi.org/10.1111/cgf.14313
  53. The Making of Continuous Colormaps. IEEE Transactions on Visualization and Computer Graphics 14, 8 (2019), 1–1. https://doi.org/10.1109/tvcg.2019.2961674
  54. Visual Aesthetics and Human Preference. Annual Review of Psychology 64, 1 (jan 2013), 77–107. https://doi.org/10.1146/annurev-psych-120710-100504
  55. Automatic Scatterplot Design Optimization for Clustering Identification. IEEE Transactions on Visualization and Computer Graphics 29, 10 (2023), 4312–4327. https://doi.org/10.1109/TVCG.2022.3189883
  56. Ghulam Jilani Quadri and Paul Rosen. 2021. Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology. IEEE Transactions on Visualization and Computer Graphics 27, 2 (2021), 1829–1839. https://doi.org/10.1109/TVCG.2020.3030365
  57. Khairi Reda. 2022. Rainbow Colormaps: What are they good and bad for? IEEE Transactions on Visualization and Computer Graphics (2022).
  58. Khairi Reda and Danielle Albers Szafir. 2021. Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference. IEEE Transactions on Visualization and Computer Graphics 27, 2 (feb 2021), 1032–1042. https://doi.org/10.1109/TVCG.2020.3030439
  59. Active preference-based learning of reward functions. In Robotics: Science and Systems, Vol. 13. MIT Press Journals. https://doi.org/10.15607/rss.2017.xiii.053
  60. ColorMoves: Real-time Interactive Colormap Construction for Scientific Visualization. IEEE Computer Graphics and Applications 38, 1 (jan 2018), 20–29. https://doi.org/10.1109/MCG.2018.011461525
  61. Semantic discriminability for visual communication. IEEE Transactions on Visualization and Computer Graphics 27, 2 (2020), 1022–1031.
  62. Color Crafting: Automating the Construction of Designer Quality Color Ramps. IEEE Transactions on Visualization and Computer Graphics 26, 1 (aug 2020), 1215–1225. https://doi.org/10.1109/TVCG.2019.2934284 arXiv:1908.00629
  63. Nathaniel J. Smith and Stefan van der Walt. 2019. viscm. https://github.com/matplotlib/viscm.
  64. VividGraph: Learning to Extract and Redesign Network Graphs From Visualization Images. IEEE Transactions on Visualization and Computer Graphics 29, 7 (2023), 3169–3181. https://doi.org/10.1109/TVCG.2022.3153514
  65. Maureen Stone. 2016. How we designed the new color palettes in Tableau 10. https://www.tableau.com/blog/colors-upgrade-tableau-10-56782?hootPostID=4574bce614837b6f773ae492b0c516f7. Accessed: 2023-04-03.
  66. Richard S Sutton and Andrew G Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA.
  67. Danielle Albers Szafir. 2017. Modeling color difference for visualization design. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2017), 392–401.
  68. Robert Endre Tarjan. 1978. Complexity of Combinatorial Algorithms. SIAM Rev. 20, 3 (jul 1978), 457–491. https://doi.org/10.1137/1020067
  69. Andrea L Thomaz and Cynthia Breazeal. 2008. Teachable robots: Understanding human teaching behavior to build more effective robot learners. Artificial Intelligence 172, 6-7 (2008), 716–737.
  70. Task-driven color coding. In 2008 12th International Conference Information Visualisation. IEEE, 373–380.
  71. Altair: interactive statistical visualizations for Python. Journal of Open Source Software 3, 32 (2018), 1057.
  72. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17 (2020), 261–272. https://doi.org/10.1038/s41592-019-0686-2
  73. Michael L. Waskom. 2021. seaborn: statistical data visualization. Journal of Open Source Software 6, 60 (2021), 3021. https://doi.org/10.21105/joss.03021
  74. Christopher John Cornish Hellaby Watkins. 1989. Learning from delayed rewards. PhD Thesis. Kings College, University of Cambridge,. http://www.cs.rhul.ac.uk/$∼$chrisw/new_thesis.pdf
  75. Christopher John Cornish Hellaby Watkins and Peter Dayan. 1992. Q-learning. Machine Learning 8, 3-4 (may 1992), 279–292. https://doi.org/10.1007/bf00992698
  76. Deep Colormap Extraction From Visualizations. IEEE Transactions on Visualization and Computer Graphics 28, 12 (2022), 4048–4060. https://doi.org/10.1109/TVCG.2021.3070876
  77. InfoColorizer: Interactive Recommendation of Color Palettes for Infographics. IEEE Transactions on Visualization and Computer Graphics 28, 12 (2022), 4252–4266. https://doi.org/10.1109/TVCG.2021.3085327
  78. Image-guided color mapping for categorical data visualization. Computational Visual Media 8, 4 (2022), 613–629.
  79. Effects of data distribution and granularity on color semantics for colormap data visualizations. In 2023 IEEE Visualization and Visual Analytics - Short Papers. https://doi.org/10.1109/VIS54172.2023.00011
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Authors (3)
  1. Matt-Heun Hong (4 papers)
  2. Zachary N. Sunberg (20 papers)
  3. Danielle Albers Szafir (21 papers)
Citations (1)
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