Gam(
this SweepableRegressionTrainers trainers,
string labelColumnName = "Label",
string featureColumnName = "Features",
SweepableOption <GamRegressionTrainer.Options> optionSweeper = null,
GamRegressionTrainer.Options defaultOption = null)
{
var context = trainers.Context;
if (optionSweeper == null)
{
optionSweeper = GamRegressionTrainerSweepableOptions.Default;
}
optionSweeper.SetDefaultOption(defaultOption);
return(context.AutoML().CreateSweepableEstimator(
(context, option) =>
{
option.LabelColumnName = labelColumnName;
option.FeatureColumnName = featureColumnName;
return context.Regression.Trainers.Gam(option);
},
optionSweeper,
new string[] { labelColumnName, featureColumnName },
new string[] { Score },
nameof(GamRegressionTrainer)));
}
/// <summary>
/// Create <see cref="GamRegressionTrainer"/> using advanced options, which predicts a target using generalized additive models (GAM).
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="options">Trainer options.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static GamRegressionTrainer Gam(this RegressionCatalog.RegressionTrainers catalog,
GamRegressionTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new GamRegressionTrainer(env, options));
}
public static PredictionEngine <TIn, TOut> GeneralizedAdditiveModel <TIn, TOut>(
IEnumerable <TIn> trainDataset,
GamRegressionTrainer.Options options,
Action <ITransformer> additionModelAction = null) where TIn : class, new() where TOut : class, new()
{
var context = new MLContext();
var model = context.RegressionTrainerTemplate(trainDataset, context.Regression.Trainers.Gam(options));
var predictionEngine = context.Model.CreatePredictionEngine <TIn, TOut>(model);
additionModelAction?.Invoke(model);
return(predictionEngine);
}
// This example requires installation of additional NuGet package
// <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
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 examples.
var examples = GenerateRandomDataPoints(1000);
// Convert the examples list to an IDataView object, which is consumable by ML.NET API.
var trainingData = mlContext.Data.LoadFromEnumerable(examples);
// Define trainer options.
var options = new GamRegressionTrainer.Options
{
// The entropy (regularization) coefficient.
EntropyCoefficient = 0.3,
// Reduce the number of iterations to 50.
NumberOfIterations = 50
};
// Define the trainer.
var pipeline = mlContext.Regression.Trainers.Gam(options);
// Train the model.
var model = pipeline.Fit(trainingData);
// Create testing examples. Use different random seed to make it different from training data.
var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, 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
foreach (var p in predictions.Take(5))
{
Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
}
// Expected output:
// Label: 0.985, Prediction: 0.841
// Label: 0.155, Prediction: 0.187
// Label: 0.515, Prediction: 0.496
// Label: 0.566, Prediction: 0.467
// Label: 0.096, Prediction: 0.144
// Evaluate the overall metrics
var metrics = mlContext.Regression.Evaluate(transformedTestData);
Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
// Expected output:
// Mean Absolute Error: 0.06
// Mean Squared Error: 0.01
// Root Mean Squared Error: 0.08
// RSquared: 0.93
}
// This example requires installation of additional NuGet
// package for Microsoft.ML.FastTree found at
// https://www.nuget.org/packages/Microsoft.ML.FastTree/
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 GamRegressionTrainer.Options
{
LabelColumnName = nameof(DataPoint.Label),
FeatureColumnName = nameof(DataPoint.Features),
// The entropy (regularization) coefficient.
EntropyCoefficient = 0.3,
// Reduce the number of iterations to 50.
NumberOfIterations = 50
};
// Define the trainer.
var pipeline =
mlContext.Regression.Trainers.Gam(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: 0.841
// Label: 0.155, Prediction: 0.187
// Label: 0.515, Prediction: 0.496
// Label: 0.566, Prediction: 0.467
// Label: 0.096, Prediction: 0.144
// Evaluate the overall metrics
var metrics = mlContext.Regression.Evaluate(transformedTestData);
PrintMetrics(metrics);
// Expected output:
// Mean Absolute Error: 0.04
// Mean Squared Error: 0.01
// Root Mean Squared Error: 0.05
// RSquared: 0.98 (closer to 1 is better. The worst case is 0)
}