Learning with F#

Learning with F#

• 1.
  LEARNING WITH F# PhillipTrelford, Applied Games, Microsoft Research
• 2.
  Overview  Learning ProbabilisticModels  Factor Graphs  Inference in Factor Graphs  Projects  TrueSkill Analysis  Internal adCenter competition  Benefits of F#
• 3.
  Overview  Learning ProbabilisticModels  Factor Graphs  Inference in Factor Graphs  Projects  TrueSkill Analysis  Internal adCenter competition  Benefits of F#
• 4.
  Factor Graphs  Bi-partitegraphs  Random variables  Factors  Two purposes:  Representation of the structure of a probability distribution (more fine grained than Bayes Nets)  Represent an algorithm where computations are performed along the edges (schedules)
• 5.
  TrueSkill™ Factor Graph s1 s1s2 s2 s3 s3 s4 s4 t1 t1 y1 2 y1 2 t2 t2 t3 t3 y2 3 y2 3
• 6.
  Inference in FactorGraphs  Computational question:  What are the marginals of the joint probability?  What is the mode of the joint probability?  Naive approach require exponential run-time:  Marginals:  Mode:
• 7.
  Message Passing inFactor Graphs w1w1 w2w2 ++ ss cc
• 8.
  Overview  Learning ProbabilisticModels  Factor Graphs  Inference in Factor Graphs  Projects  TrueSkill Analysis  Internal adCenter competition  Benefits of F#
• 9.
   Given:  Matchoutcomes: Orderings among k teams consisting of n1, n2 , ..., nk players, respectively  Questions:  Skill si for each player such that  Global ranking among all players  Fair matches between teams of players TrueSkill Rating Problem
• 10.
  Xbox 360 Live Launched in September 2005  Every game uses TrueSkill™ to match players  > 6 million players  > 1 million matches per day  > 2 billion hours of gameplay
• 11.
  Xbox Live Activityviewer  Code size: 1400 LOC + 1400 LOC  Project size: 2 project / 21 files  Development time: 2 month  Features  Parser: High performance (> 2GB logs in 1 hour)  Parser: Recreation of matchmaking server status  Viewer: SQL database integration (deep schema)
• 12.
  Xbox 360 &Halo 3  Xbox 360 Live  Launched in September 2005  Every game uses TrueSkill™ to match players  > 6 million players  > 1 million matches per day  > 2 billion hours of gameplay  Halo 3  Launched on 25th September 2007  Largest entertainment launch in history  > 500,000 player concurrently playing
• 13.
  F# Tools forHalo 3  Questions  Controllable player skill progression (slow-down!)  Controllable skill distributions (re-ordering)  Simulations  Large scale simulation of > 8,000,000,000 matches  Distributed application written in C# using .Net remoting  Tools  Result viewer (Logged results: 52 GB of data)  Real-time simulator of partial update
• 14.
  Halo 3 SimulationResult Viewer  Code size: 1800 LOC  Project size: 11 files  Development time: 2 month  Features  Multithreaded histogram viewer (due to file size)  Real-time spline editor (monotonically increasing)  Based on WinForms (compatability)
• 15.
  Halo 3 PartialUpdate Analyser  Code size: 2600 LOC  Project size: 10 files  Development time: 1 month  Features  SQL database integration (analysis of beta test data)  Full integration of C# TrueSkill code (.Net library)  Real time changes
• 16.
  Overview  Learning ProbabilisticModels  Factor Graphs  Inference in Factor Graphs  Projects  TrueSkill Analysis  Internal adCenter competition  Benefits of F#
• 17.
  The adCenter Problem Cash-cow of Search  Selling “web space” at www.live.com and www.msn.com.  “Paid Search” (prices by auctions)  The internal competition focuses on Paid Search.
• 18.
  The Internal adCenter Competition Start of competition: February 2007  Start of training phase: May 2007  End of training phase: June 2007  Task:  Predict the probability of click of a few days of real data from several weeks of training data (logged page views)  Resources:  4 (2 x 2) 64-bit CPU machine  16 GB of RAM  200 GB HD
• 19.
  The Scale ofThings  Weeks of data in training: 7,000,000,000 impressions  2 weeks of CPU time during training: 2 wks × 7 days × 86,400 sec/day = 1,209,600 seconds  Learning algorithmspeed requirement:  5,787 impression updates / sec  172.8 μs per impression update
• 20.
  Tool Chain: ExistingTools  Excel 2007  Scientific Visualisation  Small Scale Simulations  SQL Server2005  1.6 TB of “active” data (for 2 weeks of data + indices)  Ad-Hoc Queries and Stored Procedures  Visual Studio 2005 & F#  54 projects solution (many small tools)  FSI for rapid development and code testing  Strong typing as a surrogate for correctness
• 21.
  SQL Schema Generator Code size: 500 LOC  Project size: 1 file  Development time: 2 weeks  Features  Code defines the schema (unlike LINQ)!  High-performance insertion via computed bulk- insertion with automated key propagation  Code sample is now part of the F# distribution
• 22.
  Strong Typing andSQL Datastores /// A single page-view type PageView = { ClientDateTime : DateTime GmtSeconds : int TargetDomainId : int16 Medium : MediumType option StartPosition : int PageNum : byte [<SqlStringLengthAttribute(256)>] Query : string Gender : Gender option AgeBucket : AgeGroup option ReturnedAdCnt : byte AbTestingType : byte option AlgorithmId : int option ANID : int128 option GUID : int128 option [<SqlStringLengthAttribute(15)>] PassportZipCode : string option [<SqlStringLengthAttribute(2)>] PassportCountry : string option PassportRegion : int [<SqlStringLengthAttribute(2)>] PassportOccupation : char LocationCountry : int LocationState : int LocationMetroArea : int CategoryId : int16 SubCategoryId : int16 FormCode : int16 ReturnedAds : Advertisement array } /// A single page-view type PageView = { ClientDateTime : DateTime GmtSeconds : int TargetDomainId : int16 Medium : MediumType option StartPosition : int PageNum : byte [<SqlStringLengthAttribute(256)>] Query : string Gender : Gender option AgeBucket : AgeGroup option ReturnedAdCnt : byte AbTestingType : byte option AlgorithmId : int option ANID : int128 option GUID : int128 option [<SqlStringLengthAttribute(15)>] PassportZipCode : string option [<SqlStringLengthAttribute(2)>] PassportCountry : string option PassportRegion : int [<SqlStringLengthAttribute(2)>] PassportOccupation : char LocationCountry : int LocationState : int LocationMetroArea : int CategoryId : int16 SubCategoryId : int16 FormCode : int16 ReturnedAds : Advertisement array } /// Different types of media type MediumType = | PaidSearch | ContextualSearch /// A single displayed advertisement type Advertisement = { AdId : int OrderItemId : int CampDayId : int16 CampHourNum : byte ProductId : ProductType MatchType : MatchType AdLayoutId : AdLayout RelativePosition : byte DeliveryEngineRank : int16 ActualBid : int ProbabilityOfClick : int16 MatchScore : int ImpressionCnt : int ClickCnt : int ConversionCnt : int TotalCost : int } /// Different types of media type MediumType = | PaidSearch | ContextualSearch /// A single displayed advertisement type Advertisement = { AdId : int OrderItemId : int CampDayId : int16 CampHourNum : byte ProductId : ProductType MatchType : MatchType AdLayoutId : AdLayout RelativePosition : byte DeliveryEngineRank : int16 ActualBid : int ProbabilityOfClick : int16 MatchScore : int ImpressionCnt : int ClickCnt : int ConversionCnt : int TotalCost : int } /// Create the SQL schema let schema = bulkBuild ("cpidssdm18", “Cambridge", “June10") /// Try to open the CSV file and read it pageview by pageview File.OpenTextReader “HourlyRelevanceFeed.csv" |> Seq.map (fun s -> s.Split [|','|]) |> Seq.chunkBy (fun xs -> xs.[0]) |> Seq.iteri (fun i (rguid,xss) -> /// Write the current in-memory bulk to the Sql database if i % 10000 = 0 then schema.Flush () /// Get the strongly typed object from the list of CSV file lines let pageView = PageView.Parse xss /// Insert it pageView |> schema.Insert ) /// One final flush schema.Flush () /// Create the SQL schema let schema = bulkBuild ("cpidssdm18", “Cambridge", “June10") /// Try to open the CSV file and read it pageview by pageview File.OpenTextReader “HourlyRelevanceFeed.csv" |> Seq.map (fun s -> s.Split [|','|]) |> Seq.chunkBy (fun xs -> xs.[0]) |> Seq.iteri (fun i (rguid,xss) -> /// Write the current in-memory bulk to the Sql database if i % 10000 = 0 then schema.Flush () /// Get the strongly typed object from the list of CSV file lines let pageView = PageView.Parse xss /// Insert it pageView |> schema.Insert ) /// One final flush schema.Flush ()
• 23.
  Overview  Learning ProbabilisticModels  Factor Graphs  Inference in Factor Graphs  Projects  TrueSkill Analysis  Internal adCenter competition  Benefits of F#
• 24.
  Overview  Learning ProbabilisticModels  Factor Graphs  Inference in Factor Graphs  Projects  TrueSkill Analysis  Internal adCenter competition  Benefits of F#
• 25.
  Benefits of F# Four main reasons: 1. A language that both developers and researchers speak! 2. It leads to 1. “Correct” programs 2. Succinct programs 3. Highly performant code 3. Interoperability with .NET 4. It’s fun to program!