/// <summary>
/// Create <see cref="FastTreeBinaryFeaturizationEstimator"/>, which uses <see cref="FastTreeBinaryTrainer"/> to train <see cref="TreeEnsembleModelParameters"/> to create tree-based features.
/// </summary>
/// <param name="catalog">The context <see cref="TransformsCatalog"/> to create <see cref="FastTreeBinaryFeaturizationEstimator"/>.</param>
/// <param name="options">The options to configure <see cref="FastTreeBinaryFeaturizationEstimator"/>. See <see cref="FastTreeBinaryFeaturizationEstimator.Options"/> and
/// <see cref="TreeEnsembleFeaturizationEstimatorBase.OptionsBase"/> for available settings.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static FastTreeBinaryFeaturizationEstimator FeaturizeByFastTreeBinary(this TransformsCatalog catalog,
FastTreeBinaryFeaturizationEstimator.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastTreeBinaryFeaturizationEstimator(env, options));
}
public void TestFastTreeBinaryFeaturizationInPipelineWithOptionalOutputs()
{
int dataPointCount = 200;
var data = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(dataPointCount).ToList();
var dataView = ML.Data.LoadFromEnumerable(data);
dataView = ML.Data.Cache(dataView);
var trainerOptions = new FastTreeBinaryTrainer.Options
{
NumberOfThreads = 1,
NumberOfTrees = 10,
NumberOfLeaves = 4,
MinimumExampleCountPerLeaf = 10,
FeatureColumnName = "Features",
LabelColumnName = "Label"
};
var options = new FastTreeBinaryFeaturizationEstimator.Options()
{
InputColumnName = "Features",
TrainerOptions = trainerOptions,
TreesColumnName = null,
PathsColumnName = null,
LeavesColumnName = "Leaves"
};
bool isWrong = false;
try
{
var wrongPipeline = ML.Transforms.FeaturizeByFastTreeBinary(options)
.Append(ML.Transforms.Concatenate("CombinedFeatures", "Features", "Trees", "Leaves", "Paths"))
.Append(ML.BinaryClassification.Trainers.SdcaLogisticRegression("Label", "CombinedFeatures"));
var wrongModel = wrongPipeline.Fit(dataView);
}
catch
{
isWrong = true; // Only "Leaves" is produced by the tree featurizer, so accessing "Trees" and "Paths" will lead to an error.
}
Assert.True(isWrong);
var pipeline = ML.Transforms.FeaturizeByFastTreeBinary(options)
.Append(ML.Transforms.Concatenate("CombinedFeatures", "Features", "Leaves"))
.Append(ML.BinaryClassification.Trainers.SdcaLogisticRegression("Label", "CombinedFeatures"));
var model = pipeline.Fit(dataView);
var prediction = model.Transform(dataView);
var metrics = ML.BinaryClassification.Evaluate(prediction);
Assert.True(metrics.Accuracy > 0.98);
Assert.True(metrics.LogLoss < 0.05);
Assert.True(metrics.AreaUnderPrecisionRecallCurve > 0.98);
}
public void TestFastTreeBinaryFeaturizationInPipeline()
{
int dataPointCount = 200;
var data = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(dataPointCount).ToList();
var dataView = ML.Data.LoadFromEnumerable(data);
dataView = ML.Data.Cache(dataView);
var trainerOptions = new FastTreeBinaryTrainer.Options
{
NumberOfThreads = 1,
NumberOfTrees = 10,
NumberOfLeaves = 4,
MinimumExampleCountPerLeaf = 10,
FeatureColumnName = "Features",
LabelColumnName = "Label"
};
var options = new FastTreeBinaryFeaturizationEstimator.Options()
{
InputColumnName = "Features",
TreesColumnName = "Trees",
LeavesColumnName = "Leaves",
PathsColumnName = "Paths",
TrainerOptions = trainerOptions
};
var pipeline = ML.Transforms.FeaturizeByFastTreeBinary(options)
.Append(ML.Transforms.Concatenate("CombinedFeatures", "Features", "Trees", "Leaves", "Paths"))
.Append(ML.BinaryClassification.Trainers.SdcaLogisticRegression("Label", "CombinedFeatures"));
var model = pipeline.Fit(dataView);
var prediction = model.Transform(dataView);
var metrics = ML.BinaryClassification.Evaluate(prediction);
Assert.True(metrics.Accuracy > 0.98);
Assert.True(metrics.LogLoss < 0.05);
Assert.True(metrics.AreaUnderPrecisionRecallCurve > 0.98);
}
// 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 data points to be transformed.
var dataPoints = GenerateRandomDataPoints(100).ToList();
// Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
// ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
// it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
// a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms
// which needs many data passes.
dataView = mlContext.Data.Cache(dataView);
// Define input and output columns of tree-based featurizer.
string labelColumnName = nameof(DataPoint.Label);
string featureColumnName = nameof(DataPoint.Features);
string treesColumnName = nameof(TransformedDataPoint.Trees);
string leavesColumnName = nameof(TransformedDataPoint.Leaves);
string pathsColumnName = nameof(TransformedDataPoint.Paths);
// Define the configuration of the trainer used to train a tree-based model.
var trainerOptions = new FastTreeBinaryTrainer.Options
{
// Use L2Norm for early stopping.
EarlyStoppingMetric = EarlyStoppingMetric.L2Norm,
// Create a simpler model by penalizing usage of new features.
FeatureFirstUsePenalty = 0.1,
// Reduce the number of trees to 3.
NumberOfTrees = 3,
// Number of leaves per tree.
NumberOfLeaves = 6,
// Feature column name.
FeatureColumnName = featureColumnName,
// Label column name.
LabelColumnName = labelColumnName
};
// Define the tree-based featurizer's configuration.
var options = new FastTreeBinaryFeaturizationEstimator.Options
{
InputColumnName = featureColumnName,
TreesColumnName = treesColumnName,
LeavesColumnName = leavesColumnName,
PathsColumnName = pathsColumnName,
TrainerOptions = trainerOptions
};
// Define the featurizer.
var pipeline = mlContext.Transforms.FeaturizeByFastTreeBinary(options);
// Train the model.
var model = pipeline.Fit(dataView);
// Apply the trained transformer to the considered data set.
var transformed = model.Transform(dataView);
// Convert IDataView object to a list. Each element in the resulted list corresponds to a row in the IDataView.
var transformedDataPoints = mlContext.Data.CreateEnumerable <TransformedDataPoint>(transformed, false).ToList();
// Print out the transformation of the first 3 data points.
for (int i = 0; i < 3; ++i)
{
var dataPoint = dataPoints[i];
var transformedDataPoint = transformedDataPoints[i];
Console.WriteLine($"The original feature vector [{String.Join(",", dataPoint.Features)}] is transformed to three different tree-based feature vectors:");
Console.WriteLine($" Trees' output values: [{String.Join(",", transformedDataPoint.Trees)}].");
Console.WriteLine($" Leave IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Leaves)}].");
Console.WriteLine($" Paths IDs' 0-1 representation: [{String.Join(",", transformedDataPoint.Paths)}].");
}
// Expected output:
// The original feature vector [0.8173254,0.7680227,0.5581612] is transformed to three different tree-based feature vectors:
// Trees' output values: [0.5714286,0.4636412,0.535588].
// Leave IDs' 0-1 representation: [0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1].
// Paths IDs' 0-1 representation: [1,0,0,1,1,1,0,1,0,1,1,1,1,1,1].
// The original feature vector [0.5888848,0.9360271,0.4721779] is transformed to three different tree-based feature vectors:
// Trees' output values: [0.2352941,-0.1382389,0.535588].
// Leave IDs' 0-1 representation: [0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1].
// Paths IDs' 0-1 representation: [1,0,0,1,1,1,0,1,0,1,1,1,1,1,1].
// The original feature vector [0.2737045,0.2919063,0.4673147] is transformed to three different tree-based feature vectors:
// Trees' output values: [0.2352941,-0.1382389,-0.2184284].
// Leave IDs' 0-1 representation: [0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0].
// Paths IDs' 0-1 representation: [1,0,0,1,1,1,0,1,0,1,1,1,0,0,0].
}
