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mlnet.fsx
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mlnet.fsx
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#r "netstandard"
#load @".paket/load/netstandard2.0/main.group.fsx"
open Microsoft.ML
open Microsoft.ML.Data
open Microsoft.ML.Runtime.Api
open Microsoft.ML.Transforms
open Microsoft.ML.Trainers
open System
let nativeDirectory = Environment.GetFolderPath(Environment.SpecialFolder.UserProfile) + @"/.nuget/packages/microsoft.ml/0.3.0/runtimes/win-x64/native"
Environment.SetEnvironmentVariable("Path", Environment.GetEnvironmentVariable("Path") + ";" + nativeDirectory)
let testDataPath = __SOURCE_DIRECTORY__ + @"/data/imdb_labelled.txt"
type SentimentData() =
[<Column(ordinal = "0"); DefaultValue>]
val mutable SentimentText : string
[<Column(ordinal = "1", name = "Label"); DefaultValue>]
val mutable Sentiment : float32
type SentimentPrediction() =
[<ColumnName "PredictedLabel"; DefaultValue>]
val mutable Sentiment : bool
let _load =
[ typeof<Microsoft.ML.Runtime.Transforms.TextAnalytics>
typeof<Microsoft.ML.Runtime.FastTree.FastTree> ]
let pipeline = LearningPipeline()
pipeline.Add(TextLoader(testDataPath).CreateFrom<SentimentData>(useHeader = false, separator = '\t'))
pipeline.Add(TextFeaturizer("Features", [| "SentimentText" |]))
pipeline.Add(FastTreeBinaryClassifier(NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2))
let model = pipeline.Train<SentimentData, SentimentPrediction>()
let predictions =
[ SentimentData(SentimentText = "Contoso's 11 is a wonderful experience")
SentimentData(SentimentText = "Sort of ok")
SentimentData(SentimentText = "Joe versus the Volcano Coffee Company is a great film.") ]
|> model.Predict
predictions
|> Seq.iter(fun p -> printfn "%b" p.Sentiment)