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Extending Ruby by harnessing other languages
The document discusses enhancing Ruby's capabilities by integrating other programming languages, particularly focusing on using Python for numerical tasks. It highlights the strengths of Python's scientific ecosystem, including libraries like NumPy and pandas, which can complement Ruby in data processing. The author presents the challenges and solutions of utilizing Python within Ruby applications, advocating for a flexible and performant integration approach.
Extending Ruby by harnessing other languages
- 1. Extending Ruby byharnessing other languages Brendon McLean @brendon9x
- 2. What if Rubydoesn’t do everything well? But you want to use Ruby anyway
- 3. A long timeago, in a startup far, far away…
- 4. The Challenge a.k.a “Thevalue proposition”
- 5. To bring meaningfulinsight and intelligence to market research* *In two weeks please
- 7. Solution MVP on Rubyon Rails
- 8. The Requirement a.k.a “TheProblem” a.k.a Numerical Ruby Please
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- 10. RESP AGE GENDERUSE_IOS USE_ANDROID CAT_PERSON 1 18-24 M 1 1 N 2 25-35 M N 3 35-50 F 1 1 Y 4 35-50 F Y 5 18-24 M 1 1 Y 6 25-35 M 1 Y 7 25-35 M 1 1 Y 8 35-50 F 1 N 9 18-24 F 1 Y 10 18-24 F 1 N 11 35-50 F 1 N 12 35-50 F 1 N 13 18-24 F 1 Y 14 35-50 M 1 N 15 18-24 F 1 N 16 35-50 M 1 N 17 35-50 M 1 1 N N 25-35 M Y
- 11. up to 40GB* *perdataset
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- 13. Interrogate Anything* *goodbye clevercaching strategy
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- 15. Approx FLOPS 1,000 1,000,000 1,000,000,000 1,000,000,000,000 Plain Ruby(2011) Bitset (2012) GSL (2012) NArray (2013) Complexity inflection point
- 16. Performance Abstraction PowerComplexity
- 17. Time to considerother options
- 18. If, hypothetically, weweren’t using Ruby, what else is out there?
- 20. Haskell Comes with freebeard* *beard must compile
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- 23. Size of scientificcommunity
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- 25. Similar* attitude tousability and expressiveness first *ish
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- 27. Numpy • Lineage goesback to 1995 • Array computing — vectorised operations for Python • NArray is based on Numpy • Is the bedrock upon which the rest of scientific Python is built
- 28. Vectorisation $> array.reduce(&:+) $> a.zip(b).map do |l, r| l * r end $> array.sum $> a * b
- 29. Pandas • Built onNumpy • Basically ports the best bits of R into Python • Fast • Cognitively simpler for general programmers • Munging!
- 30. Bonus extras • Scipy:Linear Algebra, FFT, Clustering, Stats • IPython Notebooks • Sympy: Computer Algebra System • nltk: Natural Language Toolkit • scikit-learn: Machine Learning
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- 32. Total commits NArray GSLPandas Numpy 12,588 10,865 193141
- 33. Contributers NArray GSL NumpyPandas 310 249 44
- 34. Issues: Open andClosed GSL NArray Numpy Pandas 1051 651 181 4681 2691 185
- 35. Using Python fromRuby ❤️
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- 43. API implementation problem Gettingto Ruby to run Python === Getting Python to run Ruby
- 44. Ruby => Data=> Python* *or other
- 45. “Code is data” —people with LISP personality disorder
- 46. S-Expressions (function arg1 arg2 arg3 ...)
- 47. Simple s-expression example $> (+ 1 1) => 2 $> (find User 1 2) $> 1 + 1 => 2 $> User.find(1, 2) $> User.send(:find, 1, 2)
- 48. Example with nesting $> => 5 2 2* 1+
- 49. Example with nesting $> => 5 22*1+( ( ) )
- 50. ActiveLISP User.select(:state). where(no_spam: false). group(:state). count (count (group :state (where :no_spam false (select :state User) ) ) )
- 51. Ruby => S-Expressions S-Expressions=> Python
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- 56. Target multiple backendsthrough a common API
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- 60. Thanks • Min RKInitial iRuby Kernel • Daniel Mendler for continued work on iRuby Kernel • My Team @ Intellection • And all the gems!
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