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@Thorium Thorium/ml.fs
Created Mar 28, 2017

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Using machine learning tool Accord.Net from F#
// 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|]
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