Learning with F#
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This document discusses using F# for learning probabilistic models and projects at Microsoft. It covers factor graphs and inference in factor graphs for representing probabilistic models. It then describes two projects - analyzing the TrueSkill ranking algorithm and an internal adCenter competition. It concludes by outlining benefits of F# such as producing correct, succinct and high performance code while being fun to program in.
![Strong Typing and SQL
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 ()](https://image.slidesharecdn.com/philtrelford-130614045522-phpapp02/85/Learning-with-F-22-320.jpg)
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Learning with F#
- 1. LEARNING WITH F# Phillip Trelford, Applied Games, Microsoft Research
- 2. Overview Learning Probabilistic Models Factor Graphs Inference in Factor Graphs Projects TrueSkill Analysis Internal adCenter competition Benefits of F#
- 3. Overview Learning Probabilistic Models Factor Graphs Inference in Factor Graphs Projects TrueSkill Analysis Internal adCenter competition Benefits of F#
- 4. Factor Graphs Bi-partite graphs 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 s1 s2 s2 s3 s3 s4 s4 t1 t1 y1 2 y1 2 t2 t2 t3 t3 y2 3 y2 3
- 6. Inference in Factor Graphs 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 in Factor Graphs w1w1 w2w2 ++ ss cc
- 8. Overview Learning Probabilistic Models Factor Graphs Inference in Factor Graphs Projects TrueSkill Analysis Internal adCenter competition Benefits of F#
- 9. Given: Match outcomes: 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 Activity viewer 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 for Halo 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 Simulation Result 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 Partial Update 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 Probabilistic Models 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 of Things 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: Existing Tools 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 and SQL 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 Probabilistic Models Factor Graphs Inference in Factor Graphs Projects TrueSkill Analysis Internal adCenter competition Benefits of F#
- 24. Overview Learning Probabilistic Models 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!