public void InferColumnsColumnInfoParam()
{
var columnInfo = new ColumnInformation()
{
LabelColumnName = DatasetUtil.MlNetGeneratedRegressionLabel
};
var result = new MLContext().Auto().InferColumns(DatasetUtil.DownloadMlNetGeneratedRegressionDataset(),
columnInfo);
var labelCol = result.TextLoaderOptions.Columns.First(c => c.Name == DatasetUtil.MlNetGeneratedRegressionLabel);
Assert.Equal(DataKind.Single, labelCol.DataKind);
Assert.Equal(DatasetUtil.MlNetGeneratedRegressionLabel, result.ColumnInformation.LabelColumnName);
Assert.Single(result.ColumnInformation.NumericColumnNames);
Assert.Equal(DefaultColumnNames.Features, result.ColumnInformation.NumericColumnNames.First());
Assert.Null(result.ColumnInformation.ExampleWeightColumnName);
}
public void AutoFitRegressionTest()
{
var context = new MLContext();
var dataPath = DatasetUtil.DownloadMlNetGeneratedRegressionDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.MlNetGeneratedRegressionLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var validationData = context.Data.TakeRows(trainData, 20);
trainData = context.Data.SkipRows(trainData, 20);
var result = context.Auto()
.CreateRegressionExperiment(0)
.Execute(trainData, validationData,
new ColumnInformation()
{
LabelColumnName = DatasetUtil.MlNetGeneratedRegressionLabel
});
Assert.True(result.RunDetails.Max(i => i.ValidationMetrics.RSquared > 0.9));
}
