Am Neumarkt 😱

#DS#visualization https://percival.ink/ A new lightweight language for data analysis and visualization. It looks promising. I hate jupyter notebooks and I don't use them on most of my projects. One of the reasons is low reproducibility due to its non-reative nature. You changed some old cells and forgot to run a cell below, you may read wrong results. This new language is reactive. If old cells are changed, related results are also updated.

#ml#rl How to Train your Decision-Making AIs https://thegradient.pub/how-to-train-your-decision-making-ais/ The author reviewed "five types of human guidance to train AIs: evaluation, preference, goals, attention, and demonstrations without action labels". The last one reminds me of the movie Finch. In the movie, Finch was teaching the robot to walk by demonstrating walking but without "labels".

#visualization Hmmm my plate is way off the planetary heath diet recommendation. Source: https://www.nature.com/articles/d41586-021-03612-1

#DS Just in case you are also struggling with Python packages on Apple M1 Macs I am using the third option: anaconda + miniforge. https://www.anaconda.com/blog/apple-silicon-transition

11月25号是消除对妇女的暴力行为国际日，来自metaLab的研究人员在随机选择一百万条#MeToo推文后，仔细阅读了转发次数超过 100 次的示例，在894 条推文中只有 8 条是关于性侵犯或围绕#MeToo主题的经历的实际推文，其余绝大多数是新闻媒体和政治讨论，其中大多数都忽略了#MeToo运动核心的具体问题和幸存者的声音，设计师Kim Albrecht想通过这个可视化项目来展示被忽视的针对女性暴力问题

#visualization An interactive Visual Vocabulary: https://ft-interactive.github.io/visual-vocabulary/

#tool https://www.jetbrains.com/fleet/

#ML SHAP (SHapley Additive exPlanations) is a system of methods to interpret machine learning models. The author of SHAP built an easy-to-use package to help us understand how the features are contributing to the machine learning model predictions. The package comes with a comprehensive tutorial for different machine learning frameworks. - Python Package: [slundberg/shap](https://shap.readthedocs.io/) - A tutorial on how to use it: https://www.aidancooper.co.uk/a-non-technical-guide-to-interpreting-shap-analyses/ --- The package is so popular and you might be using it already. So what is SHAP exactly? It is a series of methods based on Shapley values. > SHAP (SHapley Additive exPlanations) is a game-theoretic approach to explain the output of any machine learning model. > > -- [slundberg/shap](https://github.com/slundberg/shap) Regarding Shapley value: There are two key ideas in calculating a Shapley value. - A method to measure the contribution to the final prediction of some certain combination of features. - A method to combine these "contributions" into a score. SHAP provides some methods to estimate Shapley values and also for different models. The following two pages explain Shapley value and SHAP thoroughly. - https://christophm.github.io/interpretable-ml-book/shap.html - https://christophm.github.io/interpretable-ml-book/shapley.html References: - Lundberg SM, Lee SI. A unified approach to interpreting model predictions. of the 31st international conference on neural …. 2017. Available: http://papers.nips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf - Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature Biomedical Engineering. 2018;2: 749–760. doi:10.1038/s41551-018-0304-0 --- I posted [a similar article years ago in our Chinese data weekly newsletter](https://github.com/data-com/weekly/discussions/27) but for a different story.

MLU-Explain - 来自亚马逊工程师 Jared Wilber的交互可视化核心机器学习概念的视觉论文#visualessay

#visualization Nicolas P. Rougier released his book on scientific visualization. He made some aesthetically pleasing figures. And the book is free. https://github.com/rougier/scientific-visualization-book

#DS#Visualization Okay, I'll tell you the reason I wrote this post. It is because xkcd made [this](https://xkcd.com/2537/). --- Choosing proper colormaps for our visualizations is important. It's almost like shooting a photo using your phone. Some phones capture details in every corner, while some phones give us overexposed photos and we get no details in the bright regions. A proper colormap should make sure we see the details we need to see. To address the importance of colormaps, we use the two examples shown on the website of colorcet[^colorcet]. The two colormaps, hot, and fire, can be found in matplotlib and colorcet, respectively. I can not post multiple images in one message, please see the full post for the comparisons of the two colormaps. Really, it is amazing. Find the link below: https://github.com/kausalflow/community/discussions/20 It is clear that "hot" brings in some overexposure. The other colormap, "fire", is a so-called perceptually uniform colormap. More experiments are performed in colorcet. Glasbey et al showed some examples of inspecting different properties using different colormaps[^Glasbey2007]. One of the methods to make sure the colormap shows enough details is to use perceptually uniform colrmaps[^Kovesi2015]. Kovesi provides a method to validate if a color map has uniform perceptual contrast[^Kovesi2015]. --- References and links mentioned in this post: [^colorcet]: Anaconda. colorcet 1.0.0 documentation. [cited 12 Nov 2021]. Available: https://colorcet.holoviz.org/ [^colorcet-github]: holoviz. colorcet/index.ipynb at master · holoviz/colorcet. In: GitHub [Internet]. [cited 12 Nov 2021]. Available: https://github.com/holoviz/colorcet/blob/master/examples/index.ipynb [^Kovesi2015]: Kovesi P. Good Colour Maps: How to Design Them. arXiv [cs.GR]. 2015. Available: http://arxiv.org/abs/1509.03700 [^Glasbey2007]: Glasbey C, van der Heijden G, Toh VFK, Gray A. Colour displays for categorical images. Color Research & Application. 2007. pp. 304–309. doi:10.1002/col.20327 [^matplotlib-colormaps]: Choosing Colormaps in Matplotlib — Matplotlib 3.4.3 documentation. [cited 12 Nov 2021]. Available: https://matplotlib.org/stable/tutorials/colors/colormaps.html

#ML#fun animegan v2! (I stole this animation from reddit. https://www.reddit.com/r/MachineLearning/comments/qo4kp8/r_p_animeganv2_face_portrait_v2/ ) Try it out: 1. Telegram bot (works pretty well): https://t.me/face2stickerbot 2. Dashboard (sometimes it doesn't work): https://huggingface.co/spaces/akhaliq/AnimeGANv2 Code: https://github.com/bryandlee/animegan2-pytorch Redditors made some funny photos too. https://www.reddit.com/r/MachineLearning/comments/qo4kp8/r_p_animeganv2_face_portrait_v2/ — This post is also available here: https://community.kausalflow.com/c/ml-applications/animeganv2