static void Main(string[] args)
{
// Create MLContext to be shared across the model creation workflow objects
// Set a random seed for repeatable/deterministic results across multiple trainings.
var mlContext = new MLContext(seed: 0);
// 1. Common data loading configuration
var textLoader = BikeSharingTextLoaderFactory.CreateTextLoader(mlContext);
var trainingDataView = textLoader.Read(TrainingDataLocation);
var testDataView = textLoader.Read(TestDataLocation);
// 2. Common data pre-process with pipeline data transformations
var dataProcessPipeline = mlContext.Transforms.CopyColumns("Count", "Label")
// Concatenate all the numeric columns into a single features column
.Append(mlContext.Transforms.Concatenate("Features", "Season", "Year", "Month",
"Hour", "Holiday", "Weekday",
"Weather", "Temperature", "NormalizedTemperature",
"Humidity", "Windspeed"));
// (Optional) Peek data in training DataView after applying the ProcessPipeline's transformations
Common.ConsoleHelper.PeekDataViewInConsole <DemandObservation>(mlContext, trainingDataView, dataProcessPipeline, 10);
Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", trainingDataView, dataProcessPipeline, 10);
// Definition of regression trainers/algorithms to use
//var regressionLearners = new (string name, IEstimator<ITransformer> value)[]
(string name, IEstimator <ITransformer> value)[] regressionLearners =
static void Main(string[] args)
{
// Create MLContext to be shared across the model creation workflow objects
// Set a random seed for repeatable/deterministic results across multiple trainings.
var mlContext = new MLContext(seed: 0);
// 1. Common data loading configuration
var textLoader = BikeSharingTextLoaderFactory.CreateTextLoader(mlContext);
var trainingDataView = textLoader.Read(TrainingDataLocation);
var testDataView = textLoader.Read(TestDataLocation);
// 2. Common data pre-process with pipeline data transformations
var dataProcessPipeline = BikeSharingDataProcessPipelineFactory.CreateDataProcessPipeline(mlContext);
// (Optional) Peek data in training DataView after applying the ProcessPipeline's transformations
Common.ConsoleHelper.PeekDataViewInConsole <DemandObservation>(mlContext, trainingDataView, dataProcessPipeline, 10);
Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", trainingDataView, dataProcessPipeline, 10);
// Definition of regression trainers/algorithms to use
//var regressionLearners = new (string name, IEstimator<ITransformer> value)[]
(string name, IEstimator <ITransformer> value)[] regressionLearners =
