Using machine learning tool Accord.Net from F#

ml.fs

// This example uses the same data and methods as
// http://accord-framework.net/docs/html/T_Accord_Statistics_Models_Regression_LogisticRegression.htm

#I @"./packages"
#r @"FSharp.Data.2.3.2/lib/net40/FSharp.Data.dll"
#r @"Accord.3.4.0/lib/net45/Accord.dll"
#r @"Accord.MachineLearning.3.4.0/lib/net45/Accord.MachineLearning.dll"
#r @"Accord.Math.3.4.0/lib/net45/Accord.Math.Core.dll"
#r @"Accord.Math.3.4.0/lib/net45/Accord.Math.dll"
#r @"Accord.Statistics.3.4.0/lib/net45/Accord.Statistics.dll"

open System
open FSharp.Data

// We have some sample data that we already know the results
// and use that to teach the machine:

(* sample.csv data content:

Age,Smokes,Had cancer
55,0,false
28,0,false
65,1,false
46,0,true
86,1,true
56,1,true
85,0,false
33,0,false
21,1,false
42,1,true

*)
#time
open Accord.Statistics.Models.Regression
open Accord.Statistics.Models.Regression.Fitting 

type People = CsvProvider<"C:\git\sample.csv",",",InferRows=2000>
let inputs, output = 
    People.Load(@"C:\git\sample.csv").Rows
    |> Seq.map (fun row -> [|float row.Age; row.Smokes |> Convert.ToDouble|], row.``Had cancer``)
    |> Seq.toArray
    |> Array.unzip

type Observation = People.Row
[<StructuredFormatDisplay("{AsString}")>]
type Feature = string * (Observation -> int)

let features:Feature[] = [|
    "Age",(fun obs -> obs.Age)
    "Smokes",(fun obs -> obs.Smokes |> Convert.ToInt32)
    |]

let learner = 
    let cancellationToken, source = 
        let s = new System.Threading.CancellationTokenSource()
        s.Token, s
    
    /// There are multiple algorithms available.
    /// For example:
    IterativeReweightedLeastSquares<LogisticRegression>(
        Tolerance = 1e-4, 
        Iterations = 1000,
        Regularization = 0.0,
        Token=cancellationToken
    )
    // Another one would be:
//    let alg = LogisticRegression(NumberOfInputs = (features |> Seq.length))
//    LogisticGradientDescent(alg, 
//        Tolerance = 0.001,
//        Iterations = 100000,
//        Token=cancellationToken)

/// Teach the model in background thread. This may take some time.
let modelTask = 
    System.Threading.Tasks.Task.Run(fun () ->
        learner.Learn(inputs, output)
    ) |> Async.AwaitTask

// When running background, you could cancel the task:
//source.Cancel()

// For now, let's just run as non-async:
let model= modelTask |> Async.RunSynchronously

// Print odds ratios:
features |> Seq.mapi(fun idx f ->
    let name = fst f
    let odds = model.GetOddsRatio(idx+1)
    name,odds )
|> Seq.sortBy snd
|> Seq.iter (printfn "%A")
// Output:
//("Age", 1.020859703)
//("Smokes", 5.858474898)

// Print estimated linear regression formula:
let formula = model.Linear.ToString()
// Output: 
// "y(x0, x1) = 0.0206451183100222*x0 + 1.76788931343272*x1 + -2.45774643623285"

// Test with current items. There is no point of course:
// You should split your sample data to two sets, and use the other to train
// the model, and the other to test the accuracy of predictions.
let items = model.Decide(inputs) |> Array.map Convert.ToDouble
// [|0.0; 0.0; 1.0; 0.0; 1.0; 1.0; 0.0; 0.0; 0.0; 1.0|]