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Big Algorithms Made Easy with Microsoft's F#

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Joel Pobar's slides from his presentation at TechEd Australia 2009

Joel Pobar's slides from his presentation at TechEd Australia 2009

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  • 1. Joel Pobar Languages Geek DEV450 http://callvirt.net/blog/post/Why-F-(TechEd-09-DEV450).aspx
  • 2. Agenda What is it? F# Intro Algorithms: Search Fuzzy Matching Classification (SVM) Recommendations Q&A
  • 3. All This in 1 hour? This is an awareness session! Lots of content, very broad, very fast You’ll get all demos, pointers, and slide deck to take offline and digest Two takeaways: F# is a great language for data Smart algorithms aren’t hard – use them, explore more!
  • 4. F# is ...a functional, object-oriented, imperative and explorative programming language for .NET what is Functional Programming?
  • 5. What is Functional Programming? Wikipedia: “A programming paradigm that treats computation as the evaluation of mathematical functions and avoids state and mutable data” -> Emphasizes functions -> Emphasizes shapes of data, rather than impl. -> Modeled on lambda calculus -> Reduced emphasis on imperative -> Safely raises level of abstraction
  • 6. Motivation for Functional Simplicity in life is good: cheaper, easier, faster, better. We typically achieve simplicity in software in two ways: By raising the level of abstraction (and OO was one design to raise abstraction) Increasing modularity Better composition and modularity == reuse Increasing signal to noise another good strategy: Communicate more in less time with more clarity
  • 7. Functional Programming Safer, while still being useful Useful C#, C++, … F# V.Next# Haskell Not Useful Unsafe Safe
  • 8. Motivation for Functional Data driven world More and more data: need higher order algorithms and techniques to derive value from data Scalability is king Economies of software scale are changing: the web requires tools + frameworks + languages that scale to millions The Multi-core (r)evolution! Need more adaptive languages + compilers to scale Language features matter!
  • 9. What is F# for? F# is a General Purpose Language Can be used for a broad range of programming tasks Superset of imperative and dynamic features Great for learning FP concepts Some particularly important domains: Financial modelling Data mining Scientific analysis Academic
  • 10. Let Type inference. The static typing of C# with Let binds values to identifiers the succinctness of a scripting language let helloWorld = “Hello, World” print_any helloWorld let myNum = 12 let myAddFunction x y = let sum = x + y sum
  • 11. Tuples Simple, very useful data structure let site1 = (“msdn.com”, 10) let site2 = (“abc.net.au”, 12) let site3 = (“news.com.au”, 22) let allSites = (site1, site2, site3) let fst (a, b) = a let snd (a, b) = b
  • 12. List, Arrays, Seq, and Options Lists and Arrays are first class citizens Options provide a some-or-nothing capability let list1 = [“Joel"; "Luke"] let array = [|2; 3; 5;|] let myseq = seq [0; 1; 2; ] let option1 = Some(“Joel") let option2 = None
  • 13. Records Simple concrete type definition type Person = { Name: string; DateOfBirth: System.DateTime; } let n = { Name = “Joel”; DateOfBirth = “13/04/81”; }
  • 14. Immutability Data is immutable by default Values may not be changed
  • 15. Discriminated Unions Great for representing the structure of data type Make = string type Model = string type Transport = | Car of Make * Model | Bicycle Both of these identifiers are of type “Transport” let me = Car (“Holden”, “Barina”) let you = Bicycle
  • 16. Functions Functions: like delegates, but unified and simple Deep type inference (fun x -> x + 1) let myFunc x = x + 1 val myFunc : int -> int let rec factorial n = if n>1 then n * factorial (n-1) else 1 let data = [5; 3; 4; 4; 5] List.sort (fun x y -> x – y) data
  • 17. Pattern Matching Helps tease apart data and data structures Works best with Unions and Records let (fst, _) = (“first”, “second”) Console.WriteLine(fst) let switchOnType(a:obj) match a with | :? Int32 -> printfn “int!” | :? Transport -> printfn “Transport“ | _ -> printfn “Everything Else!”
  • 18. F# Interactive
  • 19. Search Given a search term and a large document corpus, rank and return a list of the most relevant results…
  • 20. Blog Crawler
  • 21. Search Words Stemming? Tokenise Markup Title/Author/Date Links? A sign of strength? Let’s explore something simple
  • 22. Search Simplify: For easy machine/language manipulation … and most importantly, easy computation Vectors: natures own quality data structure Convenient machine representation (lists/arrays) Lots of existing vector math algorithms After a loving incubation moonlight incubation period, binaries moonlight 2.0 has firefox loving been released. <a linux after href=“whatever”>sour ce code</a><br><a href”something else”>FireFox binaries</a> … after 2 1 1 6 4 6 2
  • 23. Vector space: Term Count 2 9 the Document1: Linux post: 0 1 incubation Document2: Animal post: 9 2 1 crazy the 1 0 6 moonlight incubation 1 0 4 firefox crazy 6 0 6 2 linux crazy moonlight 4 1 0 2 dog the firefox 1 6 5 2 penguin dog linux 5 2 penguin penguin
  • 24. Term Count Issues incubation moonlight penguin firefox crazy linux ‘the dog penguin’ dog the Linux: 9+0+2 = 11 9 1 1 6 4 6 0 2 Animal: 2+1+5 = 8 2 0 2 0 0 0 1 5 ‘the’ is overweight Enter TF-IDF: Term Frequency Inverse Document Frequency A weight to evaluate how important a word is to a corpus i.e. if ‘the’ occurs in 98% of all documents, we shouldn’t weight it very highly in the total query
  • 25. TF-IDF Normalise the term count against the doc: tf = termCount / docWordCount Measure importance of term idf = log ( |D| / termInDocumentCount) where |D| is the total documents in the corpus tfidf = tf * idf A high weight is reached by high term frequency, and a low document frequency
  • 26. Search in under 10 minutes
  • 27. Fuzzy Matching String similarity algorithms: SoundEx; Metaphone Jaro Winkler Distance; Cosine similarity; Sellers; Euclidean distance; … We’ll look at Levenshtein Distance algorithm Defined as: The minimum edit operations which transforms string1 into string2
  • 28. Fuzzy Matching Edit costs: In-place copy – cost 0 Delete a character in string1 – cost 1 Insert a character in string2 – cost 1 Substitute a character for another – cost 1 Transform ‘kitten’ in to ‘sitting’ kitten -> sitten (cost 1 – replace k with s) sitten -> sittin (cost 1 - replace e with i) sittin -> sitting (cost 1 – add g) Levenshtein distance: 3
  • 29. Fuzzy Matching Estimated string similarity computation costs: Hard on the GC (lots of temporary strings created and thrown away, use arrays if possible. Levenshtein can be computed in O (kl) time, where ‘l’ is the length of the shortest string, and ‘k’ is the maximum distance. Parallelisable – split the set of words to compare across n cores. Can do approximately 10,000 compares per second on a standard single core laptop.
  • 30. Did You Mean?
  • 31. Classification Support Vector Machines (SVM) Supervised learning for binary classification Training Inputs: ‘in’ and ‘out’ vectors. SVM will then find a separating ‘hyperplane’ in an n-dimensional space Training costs, but classification is cheap Can retrain on the fly in some cases
  • 32. Classification
  • 33. SVM Issues Classification on 2 dimensions is easy, but most input is multi-dimensional Some ‘tricks’ are needed to transform the input data
  • 34. SVM Classifier Demo
  • 35. F# Recommendation Engine Netflix Prize - $1 million USD Must beat Netflix prediction algorithm by 10% 480k users 100 million ratings 18,000 movies Great example of deriving value out of large datasets Earns Netflix loads and loads of $$$!
  • 36. Netflix Data Format MovieId CustomerId Rating Clerks 444444 5 Clerks 2093393 4 Clerks 999 5 Clerks 8668478 1 Dogma 2432114 3 Dogma 444444 5 Dogma 999 5 ... ... ...
  • 37. Nearest Neighbour MovieId CustomerId Rating Clerks 444444 5 Clerks 2093393 4 Clerks 999 5 Clerks 8668478 1 Dogma 2432114 3 Dogma 444444 5 Dogma 999 5 ... ... ...
  • 38. Nearest Neighbour Find the best movies my neighbours agree on CustomerId 302 4418 3 56 732 444444 5 4 5 2 999 5 5 1 111211 3 5 3 66666 5 5 1212121 5 4 5656565 1 454545 5 5
  • 39. Netflix Demo
  • 40. Vector Math Made Easy A (x1,y1) B (x2,y2) C (x0,y0) If we want to calculate the distance between A and B, we call on Euclidean Distance We can represent the points in the same way using Vectors: Magnitude and Direction. Having this Vector representation, allows us to work in ‘n’ dimensions, yet still achieve Euclidean Distance/Angle calculations.
  • 41. http://callvirt.net/blog/post/Why-F-(TechEd-09-DEV450).aspx
  • 42. © 2009 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

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