FastForest(
this SweepableBinaryClassificationTrainers trainer,
string labelColumnName = "Label",
string featureColumnName = "Features",
SweepableOption <FastForestBinaryTrainer.Options> optionBuilder = null,
FastForestBinaryTrainer.Options defaultOption = null)
{
var context = trainer.Context;
if (optionBuilder == null)
{
optionBuilder = FastForestBinaryTrainerSweepableOptions.Default;
}
optionBuilder.SetDefaultOption(defaultOption);
return(context.AutoML().CreateSweepableEstimator(
(context, option) =>
{
option.LabelColumnName = labelColumnName;
option.FeatureColumnName = featureColumnName;
return context.BinaryClassification.Trainers.FastForest(option);
},
optionBuilder,
new string[] { labelColumnName, featureColumnName },
new string[] { PredictedLabel },
nameof(FastForestBinaryTrainer)));
}
/// <summary>
/// Create <see cref="FastForestBinaryTrainer"/> with advanced options, which predicts a target using a decision tree regression model.
/// </summary>
/// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
/// <param name="options">Trainer options.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static FastForestBinaryTrainer FastForest(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
FastForestBinaryTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastForestBinaryTrainer(env, options));
}
public override IEstimator <ITransformer> BuildFromOption(MLContext context, FastForestOption param)
{
var option = new FastForestBinaryTrainer.Options()
{
NumberOfTrees = param.NumberOfTrees,
LabelColumnName = param.LabelColumnName,
FeatureColumnName = param.FeatureColumnName,
ExampleWeightColumnName = param.ExampleWeightColumnName,
FeatureFraction = param.FeatureFraction,
NumberOfThreads = AutoMlUtils.GetNumberOfThreadFromEnvrionment(),
};
return(context.MulticlassClassification.Trainers.OneVersusAll(context.BinaryClassification.Trainers.FastForest(option), labelColumnName: param.LabelColumnName));
}
public void TestFastForestBinaryFeaturizationInPipeline()
{
int dataPointCount = 200;
var data = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(dataPointCount).ToList();
var dataView = ML.Data.LoadFromEnumerable(data);
dataView = ML.Data.Cache(dataView);
var trainerOptions = new FastForestBinaryTrainer.Options
{
NumberOfThreads = 1,
NumberOfTrees = 10,
NumberOfLeaves = 4,
MinimumExampleCountPerLeaf = 10,
FeatureColumnName = "Features",
LabelColumnName = "Label"
};
var options = new FastForestBinaryFeaturizationEstimator.Options()
{
InputColumnName = "Features",
TreesColumnName = "Trees",
LeavesColumnName = "Leaves",
PathsColumnName = "Paths",
TrainerOptions = trainerOptions
};
var pipeline = ML.Transforms.FeaturizeByFastForestBinary(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 > 0.97);
Assert.True(metrics.LogLoss < 0.07);
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 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 FastForestBinaryTrainer.Options
{
// Only use 80% of features to reduce over-fitting.
FeatureFraction = 0.8,
// Create a simpler model by penalizing usage of new features.
FeatureFirstUsePenalty = 0.1,
// Reduce the number of trees to 50.
NumberOfTrees = 50
};
// Define the trainer.
var pipeline = mlContext.BinaryClassification.Trainers.FastForest(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(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}, Prediction: {p.PredictedLabel}");
}
// Expected output:
// Label: True, Prediction: True
// Label: False, Prediction: False
// Label: True, Prediction: True
// Label: True, Prediction: True
// Label: False, Prediction: True
// Evaluate the overall metrics
var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(transformedTestData);
Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
// Expected output:
// Accuracy: 0.73
// AUC: 0.81
// F1 Score: 0.73
// Negative Precision: 0.77
// Negative Recall: 0.68
// Positive Precision: 0.69
// Positive Recall: 0.78
}
// 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 FastForestBinaryTrainer.Options
{
// 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 FastForestBinaryFeaturizationEstimator.Options
{
InputColumnName = featureColumnName,
TreesColumnName = treesColumnName,
LeavesColumnName = leavesColumnName,
PathsColumnName = pathsColumnName,
TrainerOptions = trainerOptions
};
// Define the featurizer.
var pipeline = mlContext.Transforms.FeaturizeByFastForestBinary(
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: [0.1111111,0.8823529].
// Leave IDs' 0-1 representation: [0,0,0,0,1,0,0,0,0,1,0].
// Paths IDs' 0-1 representation: [1,1,1,1,1,1,0,1,0].
// The original feature vector [0.5888848,0.9360271,0.4721779] is
// transformed to three different tree-based feature vectors:
// Trees' output values: [0.4545455,0.8].
// Leave IDs' 0-1 representation: [0,0,0,1,0,0,0,0,0,0,1].
// Paths IDs' 0-1 representation: [1,1,1,1,0,1,0,1,1].
// The original feature vector [0.2737045,0.2919063,0.4673147] is
// transformed to three different tree-based feature vectors:
// Trees' output values: [0.4545455,0.1111111].
// Leave IDs' 0-1 representation: [0,0,0,1,0,0,1,0,0,0,0].
// Paths IDs' 0-1 representation: [1,1,1,1,0,1,0,1,1].
}
