[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // LightGBM is 64-bit only
public void LightGbmRegression()
{
var env = new MLContext(seed: 0);
var dataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
var dataSource = new MultiFileSource(dataPath);
var ctx = new RegressionContext(env);
var reader = TextLoader.CreateReader(env,
c => (label: c.LoadFloat(11), features: c.LoadFloat(0, 10)),
separator: ';', hasHeader: true);
LightGbmRegressionModelParameters pred = null;
var est = reader.MakeNewEstimator()
.Append(r => (r.label, score: ctx.Trainers.LightGbm(r.label, r.features,
numBoostRound: 10,
numLeaves: 5,
onFit: (p) => { pred = p; })));
var pipe = reader.Append(est);
Assert.Null(pred);
var model = pipe.Fit(dataSource);
Assert.NotNull(pred);
// 11 input features, so we ought to have 11 weights.
VBuffer <float> weights = new VBuffer <float>();
pred.GetFeatureWeights(ref weights);
Assert.Equal(11, weights.Length);
var data = model.Read(dataSource);
var metrics = ctx.Evaluate(data, r => r.label, r => r.score, new PoissonLoss());
// Run a sanity check against a few of the metrics.
Assert.InRange(metrics.L1, 0, double.PositiveInfinity);
Assert.InRange(metrics.L2, 0, double.PositiveInfinity);
Assert.InRange(metrics.Rms, 0, double.PositiveInfinity);
Assert.Equal(metrics.Rms * metrics.Rms, metrics.L2, 5);
Assert.InRange(metrics.LossFn, 0, double.PositiveInfinity);
}
public static void Example()
{
// Downloading a regression dataset from github.com/dotnet/machinelearning
// this will create a housing.txt file in the filsystem.
// You can open the file to see the data.
string dataFile = SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset();
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var mlContext = new MLContext();
// Creating a data loader, based on the format of the data
var loader = TextLoaderStatic.CreateLoader(mlContext, c => (
label: c.LoadFloat(0),
features: c.LoadFloat(1, 6)
),
separator: '\t', hasHeader: true);
// Load the data, and leave 10% out, so we can use them for testing
var data = loader.Load(new MultiFileSource(dataFile));
var(trainData, testData) = mlContext.Data.TrainTestSplit(data, testFraction: 0.1);
// The predictor that gets produced out of training
LightGbmRegressionModelParameters pred = null;
// Create the estimator
var learningPipeline = loader.MakeNewEstimator()
.Append(r => (r.label, score: mlContext.Regression.Trainers.LightGbm(
r.label,
r.features,
numberOfLeaves: 4,
minimumExampleCountPerLeaf: 6,
learningRate: 0.001,
onFit: p => pred = p)
)
);
// Fit this pipeline to the training data
var model = learningPipeline.Fit(trainData);
// Check the weights that the model learned
VBuffer <float> weights = default;
pred.GetFeatureWeights(ref weights);
var weightsValues = weights.GetValues();
Console.WriteLine($"weight 0 - {weightsValues[0]}");
Console.WriteLine($"weight 1 - {weightsValues[1]}");
// Evaluate how the model is doing on the test data
var dataWithPredictions = model.Transform(testData);
var metrics = mlContext.Regression.Evaluate(dataWithPredictions, r => r.label, r => r.score);
Console.WriteLine($"L1 - {metrics.MeanAbsoluteError}"); // 4.9669731
Console.WriteLine($"L2 - {metrics.MeanSquaredError}"); // 51.37296
Console.WriteLine($"LossFunction - {metrics.LossFunction}"); // 51.37296
Console.WriteLine($"RMS - {metrics.RootMeanSquaredError}"); // 7.167493
Console.WriteLine($"RSquared - {metrics.RSquared}"); // 0.079478
}
