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Language Safety Score using Logistic Regression
(*
This is a reaction to this blog post by Steve Shogren:
http://deliberate-software.com/safety-rank-part-2/
*)
#I @"../packages"
#r @"Accord.3.0.1-alpha\lib\net45\Accord.dll"
#r @"Accord.MachineLearning.3.0.1-alpha\lib\net45\Accord.MachineLearning.dll"
#r @"Accord.Math.3.0.1-alpha\lib\net45\Accord.Math.dll"
#r @"Accord.Statistics.3.0.1-alpha\lib\net45\Accord.Statistics.dll"
let language, bugrate, criteria =
[| "F#", 0.023486288, [|1.;1.;1.;0.;1.;1.;1.;0.;0.;0.;1.;1.;1.;0.|]
"Haskell", 0.015551204, [|1.;1.;1.;0.;1.;1.;1.;1.;1.;0.;1.;1.;0.;1.|]
"Javascript", 0.039445132, [|0.;0.;0.;0.;0.;0.;0.;0.;0.;0.;1.;0.;1.;0.|]
"CoffeeScript", 0.047242288, [|0.;0.;0.;0.;0.;0.;0.;0.;0.;0.;1.;0.;1.;0.|]
"Clojure", 0.011503478, [|0.;1.;0.;0.;0.;0.;1.;0.;1.;1.;1.;0.;0.;0.|]
"C#", 0.03261284, [|0.;0.;1.;0.;0.;1.;1.;0.;0.;0.;1.;0.;1.;0.|]
"Python", 0.02531419, [|0.;0.;0.;0.;0.;0.;0.;0.;0.;0.;1.;0.;1.;0.|]
"Java", 0.032567736, [|0.;0.;0.;0.;0.;0.;0.;0.;0.;0.;1.;0.;1.;0.|]
"Ruby", 0.020303702, [|0.;0.;0.;0.;0.;0.;0.;0.;0.;0.;1.;0.;1.;0.|]
"Scala", 0.01904762, [|1.;1.;1.;0.;1.;1.;1.;0.;0.;0.;1.;0.;0.;0.|]
"Go", 0.024698375, [|0.;0.;1.;0.;0.;1.;1.;0.;0.;0.;1.;0.;1.;0.|]
"PHP", 0.031669293, [|0.;0.;0.;0.;0.;0.;0.;0.;0.;0.;1.;0.;1.;0.|] |]
|> Array.unzip3
open Accord.Statistics.Models.Regression
open Accord.Statistics.Models.Regression.Fitting
let features = 14
let model = LogisticRegression(features)
let learner = LogisticGradientDescent(model)
let rec learn () =
let delta = learner.Run(criteria, bugrate)
if delta > 0.0001
then learn ()
else ignore ()
learn () |> ignore
for i in 0 .. (language.Length - 1) do
let lang = language.[i]
let predicted = model.Compute(criteria.[i])
let real = bugrate.[i]
printfn "%16s Real: %.3f Pred: %.3f" lang real predicted
#load "FSharp.Charting.0.90.12\FSharp.Charting.fsx"
open FSharp.Charting
let last = language.Length - 1
Chart.Combine [
Chart.Line ([ for i in 0 .. last -> bugrate.[i]], "Real", Labels=language)
Chart.Line ([ for i in 0 .. last -> model.Compute(criteria.[i])], "Pred") ]
|> Chart.WithLegend()
let criteriaNames = [|
"Null Variable Usage"
"Null List Iteration"
"Prevent Variable Reuse"
"Ensure List Element Exists"
"Safe Type Casting"
"Passing Wrong Type"
"Misspelled Method"
"Missing Enum Value"
"Variable Mutation"
"Prevent Deadlocks"
"Memory Deallocation"
"Tail Call Optimization"
"Guaranteed Code Evaluation"
"Functional Purity" |]
for i in 0 .. (features - 1) do
let name = criteriaNames.[i]
let wald = model.GetWaldTest(i)
let odds = model.GetOddsRatio(i)
(printfn "%28s odds: %4.2f significant: %b" name odds wald.Significant)
[ for i in 0 .. (features - 1) ->
let name = criteriaNames.[i]
let odds = model.GetOddsRatio(i)
name,odds ]
|> List.sortBy snd
|> List.iter (printfn "%A")
@Thorium
Thorium commented Mar 22, 2017

With Accord.3.4.0 you can do things more functional style and don't have to write the learn-loop manually:

let alg = LogisticRegression(NumberOfInputs = features)
let learner = LogisticGradientDescent(alg, Iterations=10000)
let model = learner.Learn(criteria, bugrate)
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