Program.fs

open System
open System.IO
open Microsoft.ML
open Microsoft.ML.Data

/// The DemandObservation class holds one single bike demand observation record.
[<CLIMutable>]
type DemandObservation = {
    [<LoadColumn(2)>] Season : float32
    [<LoadColumn(3)>] Year : float32
    [<LoadColumn(4)>] Month : float32
    [<LoadColumn(5)>] Hour : float32
    [<LoadColumn(6)>] Holiday : float32
    [<LoadColumn(7)>] Weekday : float32
    [<LoadColumn(8)>] WorkingDay : float32
    [<LoadColumn(9)>] Weather : float32
    [<LoadColumn(10)>] Temperature : float32
    [<LoadColumn(11)>] NormalizedTemperature : float32
    [<LoadColumn(12)>] Humidity : float32
    [<LoadColumn(13)>] Windspeed : float32
    [<LoadColumn(16)>] [<ColumnName("Label")>] Count : float32
}

/// The DemandPrediction class holds one single bike demand prediction.
[<CLIMutable>]
type DemandPrediction = {
    [<ColumnName("Score")>] PredictedCount : float32;
}

// file paths to data files (assumes os = windows!)
let dataPath = sprintf "%s\\bikedemand.csv" Environment.CurrentDirectory

/// The main application entry point.
[<EntryPoint>]
let main argv =

    // create the machine learning context
    let context = new MLContext();

    // load the dataset
    let data = context.Data.LoadFromTextFile<DemandObservation>(dataPath, hasHeader = true, separatorChar = ',')

    // split the dataset into 80% training and 20% testing
    let partitions = context.Data.TrainTestSplit(data, testFraction = 0.2)

    // build a training pipeline
    let pipeline = 
        EstimatorChain()
        
            // step 1: concatenate all feature columns
            .Append(context.Transforms.Concatenate("Features", "Season", "Year", "Month", "Hour", "Holiday", "Weekday", "WorkingDay", "Weather", "Temperature", "NormalizedTemperature", "Humidity", "Windspeed"))
                                    
            // step 2: cache the data to speed up training
            .AppendCacheCheckpoint(context)

            // step 3: use a fast forest learner
            .Append(context.Regression.Trainers.FastForest(numberOfLeaves = 20, numberOfTrees = 100, minimumExampleCountPerLeaf = 10))

    // train the model
    let model = partitions.TrainSet |> pipeline.Fit

    // evaluate the model
    let metrics = partitions.TestSet |> model.Transform |> context.Regression.Evaluate

    // show evaluation metrics
    printfn "Model metrics:"
    printfn "  RMSE:%f" metrics.RootMeanSquaredError
    printfn "  MSE: %f" metrics.MeanSquaredError
    printfn "  MAE: %f" metrics.MeanAbsoluteError

    // set up a sample observation
    let sample ={
        Season = 3.0f
        Year = 1.0f
        Month = 8.0f
        Hour = 10.0f
        Holiday = 0.0f
        Weekday = 4.0f
        WorkingDay = 1.0f
        Weather = 1.0f
        Temperature = 0.8f
        NormalizedTemperature = 0.7576f
        Humidity = 0.55f
        Windspeed = 0.2239f
        Count = 0.0f // the field to predict
    }

    // create a prediction engine
    let engine = context.Model.CreatePredictionEngine model

    // make the prediction
    let prediction = sample |> engine.Predict

    // show the prediction
    printfn "\r"
    printfn "Single prediction:"
    printfn "  Predicted bike count: %f" prediction.PredictedCount

    0 // return value