Using Ruby in data science

My talk slide will be presented in RubyConf 2017

Transcript

1. Using Ruby in data science Kenta Murata, Speee Inc. Fri, Nov 17, 2017 RubyConf 2017 in New Orleans, LA, US This Slide is available at https://speakerdeck.com/mrkn/using-ruby-in- data-science 1 / 56
2. How many people related to data science? Data scientists Data engineers Developers of applications that utilize data 2 / 56
3. We need to be familiar with the way of data utilization 3 / 56
4. But Ruby hinders us from becoming famil- iar with data science because Ruby is di - cult to use in data science 4 / 56
5. The situation is changing recently 5 / 56
6. Now Ruby is getting easier to use with data science little by little 6 / 56
7. Today I'll talk about how we can use Ruby in data science 7 / 56
8. self . introduction Kenta Murata @mrkn (github, twitter, etc.) moo-ra-ken Full-time CRuby committer and Researcher at Speee Inc. bigdecimal, enumerable-statistics, pycall, mxnet.rb, etc. 8 / 56
9. 9 / 56
10. "Speee" == "Speed".succ 10 / 56
11. "Speee" == "Speed".succ faster than fast what “speed” means 11 / 56
12. "Speee" == "Speed".succ faster than fast what “speed” means iterate business trial cycles in overwhelming speed 12 / 56
13. Full-time CRuby committer My company employs me as a full-time CRuby committer. I'm permitted by my company to do any great things for Ruby ecosystem. In this year, I'm totally working for making tools for data science that are used with ap- plications written in Ruby 13 / 56
14. Topics in this talk 1. The current status of Ruby in data science 2. The patterns to use Ruby in data science 3. The future perspective 4. Conclusion 14 / 56
15. The current status of Ruby in data science 15 / 56
16. Three major projects for data science in Ruby 16 / 56
17. The 1st project: SciRuby NMatrix Daru, Daru-IO, Daru-View RB-GSL GnuplotRB Statsample Mixed_models ArrayFire etc. 17 / 56
18. http://gems.sciruby.com/ 18 / 56
19. SciRuby's bene ts and drawbacks Bene ts You only need Ruby NMatrix supports in-memory sparse matrices. But supported operations are limited. You can use data frames with Daru. 19 / 56
20. Data frames The basic data structure to manipulate and vi- sualize living data in data science. 2D table data structure like a SQL table. In Ruby, we can use data frames with Daru (or Pandas via pycall as described later). 20 / 56
21. SciRuby's bene ts and drawbacks Drawbacks NMatrix is slow for large amount of data [NMa- trix#362] Daru is less functionality for practical data sci- ence works. Less documented, so difficult to use. Reason of Drawbacks The small population of developers and users. 21 / 56
22. The 2nd project: Ruby Numo Numo::NArray Numo::FFTE Numo::FFTW Numo::Gnuplot Numo::GSL Numo::Linalg 22 / 56
23. Ruby Numo's bene ts and drawbacks Bene ts You need only Ruby Numo::NArray is faster than NMatrix and pure Ruby Drawbacks No spare matrices support No data frame support Less documented. 23 / 56
24. For the details of Ruby Numo You can watch Masa Tanaka's talk in RubyKaigi 2017 at http://rubykaigi.org/2017/presentations/masa16- tanaka.html 24 / 56
25. Which SciRuby or Ruby Numo is better? For data science (w/o other languages) SciRuby is better because it has Daru For scientific computing Ruby Numo is better because nmatrix is too slow 25 / 56
26. The 3rd project: Ruby Data Tools red-arrow red-chainer red-arrow-nmatrix red-arrow-numo-narray red-arrow-pycall 26 / 56
27. Ruby Data Tools bene ts and drawbacks Bene ts It supports Apache Arrow. The core developer, Kohei Suto, is a member of Apache Arrow's PMC. Drawbacks Too young to use in production. Now only support data I/O, data manipulation is not supported. 27 / 56
28. It is hard to do data science by only Ruby 28 / 56
29. Almost all data scientists shouldn't want to use Ruby in their jobs 29 / 56
30. Because they need the biggest powers of standard data tools in Python and R, espe- cially in exploratory data analysis 30 / 56
31. Ruby and Ruby on Rails are best for writing business web applications. 31 / 56
32. You should use Ruby and other languages like Python together 32 / 56
33. pycall 33 / 56
34. What is pycall? Pycall allows you to use Python libraries from your Ruby code very naturally. Pycall consists of two parts: The Ruby binding library of libpython.so Object-oriented protocol gateway between Ruby and Python 34 / 56
35. Example use of pycall: This example uses numpy via pycall. [1] pry(main)> require 'numpy' => true [2] pry(main)> x = Numpy.arange(2 * 3).reshape([2, 3]) => array([[0, 1, 2], [3, 4, 5]]) [3] pry(main)> y = Numpy.arange(3 * 4).reshape([3, 4]) => array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) [4] pry(main)> z = x.dot y => array([[20, 23, 26, 29], [56, 68, 80, 92]]) [5] pry(main)> z.shape => (2, 4) 35 / 56
36. Pycall family wrapper gems The following gems are available: numpy, pandas, matplotlib The following gems are future works: scikit-learn, seaborn, bokeh, keras, etc. You can use any Python libraries without wrapper gems 36 / 56
37. Demonstration 37 / 56
38. Resources about pycall I used in RubyKaigi 2017 Demonstrations
39. Other example usage of pycall Blog posts about scikit-learn examples by Soren D Using the scikit-learn machine learning li- brary in Ruby using PyCall Implementing OCR using a Random Forest Classifier in Ruby Mai Nguyen's workshop material in KiwiRuby conference
40. Pycall provides us access to Python's data tools 40 / 56
41. You can use all the following tools from your Ruby code numpy, pandas, pillow, matplotlib, bokeh, holoviews, scikit-learn, scikit-image, keras, tensor ow, etc. 41 / 56
42. The current best patterns to use Ruby in data science 42 / 56
43. You should use Ruby and other languages together Because: Almost all data scientists shouldn't want to use Ruby in their jobs They need the biggest powers of standard data tools like pandas in exploratory data analysis Ruby and Ruby on Rails are best for writing business web applications. 43 / 56
44. Three implementation patterns I proposes three implementation patterns to inte- grate application written in Ruby and data process- ing systems written in Python 1. Referring the same database directly 2. RPC by serialized data like JSON 3. Directly call by pycall 44 / 56
45. 1. Referring the same database directly 45 / 56
46. 2. RPC by serialized data like JSON 46 / 56
47. 3. Directly call by pycall 47 / 56
48. Choose the right pattern according to the situation. 1. Referring the same database directly 2. RPC by serialized data like JSON 3. Directly call by pycall 48 / 56
49. The future perspective 49 / 56
50. Two topics about the future Apache Arrow GPGPU and deep learning 50 / 56
51. Apache Arrow and Red Data Tools Apache Arrow will be the core of almost data tools. Pandas 2.0 will employ Apache Arrow as its core. PySpark already uses Apache Arrow to ex- change data between Python and Spark Red Data Tools is important for the future of Ruby's data science ecosystem. You should join Red Data Tools project if you are interested in Apache Arrow. https://red-data-tools.github.io/ 51 / 56
52. GPGPU Now we have ArrayFire by @prasunanand He is also making RbCUDA in RubyGrant 2017 @sonots will make Cumo, that is Cupy clone for Numo::NArray, in RubyGrant 2017 52 / 56
53. Deep Learning We already have tensorflow.rb written by @Arafatk In Red Data Tools, @hatappi started to make RedChainer, that is Chainer clone I'm working for writing Ruby binding of MXNet 53 / 56
54. Conclusion 54 / 56
55. Conclusion I described three major projects in Ruby about data science I demonstrated an example usage of pycall I illustrates three patterns to integrate applica- tion written in Ruby and data processing system written in Python I talked about the future perspective 55 / 56
56. Docker image to try data tools for Ruby We prepared docker image for you to try data tools for Ruby. $ docker run -it --rm -p 8888:8888 -v $(pwd):/home/jovyan/work rubydata/notebooks 56 / 56