/// <summary>
/// Create <see cref="OlsTrainer"/> with advanced options, which predicts a target using a linear regression model.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="options">Algorithm advanced options. See <see cref="OlsTrainer.Options"/>.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OlsTrainer Ols(
this RegressionCatalog.RegressionTrainers catalog,
OlsTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return new OlsTrainer(env, options);
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="OlsTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column.</param>
/// <param name="featureColumnName">The name of the feature column.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static OlsTrainer Ols(this RegressionCatalog.RegressionTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
var options = new OlsTrainer.Options
{
LabelColumnName = labelColumnName,
FeatureColumnName = featureColumnName,
ExampleWeightColumnName = exampleWeightColumnName
};
return(new OlsTrainer(env, options));
}
public static void Example()
{
// Create a new context for ML.NET operations. It can be used for
// exception tracking and logging, as a catalog of available operations
// and as the source of randomness. Setting the seed to a fixed number
// in this example to make outputs deterministic.
var mlContext = new MLContext(seed: 0);
// Create a list of training data points.
var dataPoints = GenerateRandomDataPoints(1000);
// Convert the list of data points to an IDataView object, which is
// consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
// Define trainer options.
var options = new OlsTrainer.Options
{
LabelColumnName = nameof(DataPoint.Label),
FeatureColumnName = nameof(DataPoint.Features),
// Larger values leads to smaller (closer to zero) model parameters.
L2Regularization = 0.1f,
// Whether to compute standard error and other statistics of model
// parameters.
CalculateStatistics = false
};
// Define the trainer.
var pipeline =
mlContext.Regression.Trainers.Ols(options);
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing data. Use different random seed to make it different
// from training data.
var testData = mlContext.Data.LoadFromEnumerable(
GenerateRandomDataPoints(5, seed: 123));
// Run the model on test data set.
var transformedTestData = model.Transform(testData);
// Convert IDataView object to a list.
var predictions = mlContext.Data.CreateEnumerable <Prediction>(
transformedTestData, reuseRowObject: false).ToList();
// Look at 5 predictions for the Label, side by side with the actual
// Label for comparison.
foreach (var p in predictions)
{
Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
}
// Expected output:
// Label: 0.985, Prediction: 0.960
// Label: 0.155, Prediction: 0.075
// Label: 0.515, Prediction: 0.456
// Label: 0.566, Prediction: 0.499
// Label: 0.096, Prediction: 0.080
// Evaluate the overall metrics
var metrics = mlContext.Regression.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Mean Absolute Error: 0.05
// Mean Squared Error: 0.00
// Root Mean Squared Error: 0.06
// RSquared: 0.97 (closer to 1 is better. The worst case is 0)
}
