PCA analysis on more real data with Accord.Net with Deedle and FSharp.Charting.

SecomPCA.fsx

// data from http://archive.ics.uci.edu/ml/machine-learning-databases/secom/
// reference accord framework
#r "../packages/Accord.3.0.2/lib/net45/Accord.dll"
#r "../packages/Accord.Controls.3.0.2/lib/net45/Accord.Controls.dll"
#r "../packages/Accord.IO.3.0.2/lib/net45/Accord.IO.dll"
#r "../packages/Accord.Math.3.0.2/lib/net45/Accord.Math.dll"
#r "../packages/Accord.Statistics.3.0.2/lib/net45/Accord.Statistics.dll"
//reference deelde with fsharp charting
#r "../packages/Deedle.1.2.4/lib/net40/Deedle.dll"
#r "../packages/FSharp.Charting.0.90.12/lib/net40/FSharp.Charting.dll"
#I "../packages/FSharp.Charting.0.90.12"
#I "../packages/Deedle.1.2.4"
#load "FSharp.Charting.fsx"
#load "Deedle.fsx"

open Deedle
open System
open Accord
open Accord.Controls
open Accord.Math
open Accord.Math.Comparers
open Accord.Math.Decompositions
open Accord.Statistics
open Accord.Statistics.Analysis
open FSharp.Charting

let frame1 = Frame.ReadCsv(__SOURCE_DIRECTORY__ + "../../data/secom.data.txt", hasHeaders = false, separators = " ")

// replace missing data by mean values
frame1.ColumnKeys
|> Seq.map(
    fun key -> 
        let column = frame1.GetColumn(key)
        // compure mean
        let mean = Stats.mean column
        (key,mean))
|> Seq.iter(fun (key, mean) ->
    let column = frame1.GetColumn(key)
    let newColumn = column.FillMissing mean
    frame1.ReplaceColumn(key, newColumn))


// get data form frame
let matrix : float [,] = frame1.ToArray2D()

let pca = new PrincipalComponentAnalysis(matrix)

// Also we can set to use the analysis by correlation, which is more indicated when analysing data with high different measurement units
pca.Method = AnalysisMethod.Standardize

// and just compute
pca.Compute();

// show components proportions
Chart.Line(pca.ComponentProportions).WithXAxis(Max=20.0)

// transform data if needed
let pcaFinalData = pca.Transform(matrix)