static void Main(string[] args)
{
MLContext mlContext = new MLContext();
IDataView trainDataView = mlContext.Data.LoadFromTextFile <TrafficData>(GetAbsolutePath("../../../Data/Metro_Interstate_Traffic_Volume.csv"), hasHeader: true, separatorChar: ',');
//configure experiment settings
var experimentSettings = new RegressionExperimentSettings();
experimentSettings.MaxExperimentTimeInSeconds = 10;
var cts = new CancellationTokenSource();
experimentSettings.CancellationToken = cts.Token;
experimentSettings.OptimizingMetric = RegressionMetric.MeanSquaredError;
experimentSettings.CacheDirectory = null;
// Cancel experiment after the user presses any key
CancelExperimentAfterAnyKeyPress(cts);
//create experiment
RegressionExperiment experiment = mlContext.Auto().CreateRegressionExperiment(experimentSettings);
var handler = new RegressionExperimentProgressHandler();
//execute experiment
ExperimentResult <RegressionMetrics> experimentResult = experiment.Execute(trainDataView, labelColumnName: "Label", progressHandler: handler);
//Evaluate
RegressionMetrics metrics = experimentResult.BestRun.ValidationMetrics;
Console.WriteLine($"Best Algorthm: {experimentResult.BestRun.TrainerName}");
Console.WriteLine($"R-Squared: {metrics.RSquared:0.##}");
Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:0.##}");
Console.ReadKey();
}
public void Start()
{
//Infer columns and load train data
var columnInferenceResult = mlContext.Auto().InferColumns(
path: TRAIN_DATA_FILEPATH,
labelColumnName: "next",
groupColumns: false);
TextLoader textLoader = mlContext.Data.CreateTextLoader(columnInferenceResult.TextLoaderOptions);
trainData = textLoader.Load(TRAIN_DATA_FILEPATH);
//Modify infered columns information
columnInformation = columnInferenceResult.ColumnInformation;
columnInformation.CategoricalColumnNames.Add("productId");
columnInformation.NumericColumnNames.Remove("productId");
columnInformation.CategoricalColumnNames.Add("year");
columnInformation.NumericColumnNames.Remove("year");
columnInformation.NumericColumnNames.Remove("units");
columnInformation.IgnoredColumnNames.Add("units");
var experimentSettings = new RegressionExperimentSettings()
{
MaxExperimentTimeInSeconds = 10,
OptimizingMetric = RegressionMetric.RootMeanSquaredError,
CacheDirectory = new DirectoryInfo(CACHE_DIRECTORY),
CancellationToken = cancelationTokenSource.Token
};
//Exclude trainers from experiment
experimentSettings.Trainers.Remove(RegressionTrainer.Ols);
RegressionExperiment experiment = mlContext.Auto().CreateRegressionExperiment(experimentSettings);
ExperimentResult <RegressionMetrics> experimentResult = experiment.Execute(
trainData: trainData,
columnInformation: columnInformation,
progressHandler: new RegressionProgressHandler(),
preFeaturizer: null);
ITransformer model = experimentResult.BestRun.Model;
IEstimator <ITransformer> estimator = experimentResult.BestRun.Estimator;
//Make batch predictions
IDataView predictionsDataView = model.Transform(trainData);
PrintPredictions(predictionsDataView);
PrintPredictionsEnumerable(predictionsDataView);
model = estimator.Fit(trainData);
mlContext.Model.Save(model, trainData.Schema, MODEL_FILEPATH);
Console.WriteLine("Done");
}
