/// <summary>
/// Predict a target using a decision tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="options">Advanced options to the algorithm.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmRegressorTrainer LightGbm(this RegressionCatalog.RegressionTrainers catalog,
Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmRegressorTrainer(env, options);
}
/// <summary>
/// Predict a target using generalized additive models trained with the <see cref="RegressionGamTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="options">Algorithm advanced settings.</param>
public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCatalog.RegressionTrainers catalog,
RegressionGamTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new RegressionGamTrainer(env, options));
}
/// <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));
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.PoissonRegression"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
public static PoissonRegression PoissonRegression(this RegressionCatalog.RegressionTrainers catalog, PoissonRegression.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new PoissonRegression(env, options));
}
/// <summary>
/// Predict a target using a linear regression model trained with the SDCA trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
public static SdcaRegressionTrainer StochasticDualCoordinateAscent(this RegressionCatalog.RegressionTrainers catalog,
SdcaRegressionTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new SdcaRegressionTrainer(env, options));
}
/// <summary>
/// Predict a target using a decision tree regression model trained with the <see cref="FastTreeRegressionTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="options">Algorithm advanced settings.</param>
public static FastTreeRegressionTrainer FastTree(this RegressionCatalog.RegressionTrainers catalog,
FastTreeRegressionTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastTreeRegressionTrainer(env, options));
}
/// <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="OnlineGradientDescentTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="options">Advanced arguments to the algorithm.</param>
public static OnlineGradientDescentTrainer OnlineGradientDescent(this RegressionCatalog.RegressionTrainers catalog,
OnlineGradientDescentTrainer.Options options)
{
Contracts.CheckValue(catalog, nameof(catalog));
Contracts.CheckValue(options, nameof(options));
var env = CatalogUtils.GetEnvironment(catalog);
return(new OnlineGradientDescentTrainer(env, options));
}
/// <summary>
/// Predict a target using a decision tree regression model trained with the <see cref="FastForestRegression"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The featureColumn column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
public static FastForestRegression FastForest(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastForestRegression(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves));
}
/// <summary>
/// Predict a target using a decision tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The features column.</param>
/// <param name="weights">The weights column.</param>
/// <param name="numLeaves">The number of leaves to use.</param>
/// <param name="numBoostRound">Number of iterations.</param>
/// <param name="minDataPerLeaf">The minimal number of documents allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmRegressorTrainer LightGbm(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int? numLeaves = null,
int? minDataPerLeaf = null,
double? learningRate = null,
int numBoostRound = Options.Defaults.NumBoostRound)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return new LightGbmRegressorTrainer(env, labelColumn, featureColumn, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound);
}
/// <summary>
/// Predict a target using generalized additive models trained with the <see cref="RegressionGamTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The featureColumn column.</param>
/// <param name="weights">The optional weights column.</param>
/// <param name="numIterations">The number of iterations to use in learning the features.</param>
/// <param name="learningRate">The learning rate. GAMs work best with a small learning rate.</param>
/// <param name="maxBins">The maximum number of bins to use to approximate features.</param>
public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
int numIterations = GamDefaults.NumIterations,
double learningRate = GamDefaults.LearningRates,
int maxBins = GamDefaults.MaxBins)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new RegressionGamTrainer(env, labelColumn, featureColumn, weights, numIterations, learningRate, maxBins));
}
/// <summary>
/// Predict a target using a decision tree regression model trained with the <see cref="FastForestRegression"/>.
/// </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>
/// <param name="numberOfLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="numberOfTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
public static FastForestRegression FastForest(this RegressionCatalog.RegressionTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int numberOfLeaves = Defaults.NumberOfLeaves,
int numberOfTrees = Defaults.NumberOfTrees,
int minDatapointsInLeaves = Defaults.MinimumExampleCountPerLeaf)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastForestRegression(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, numberOfTrees, minDatapointsInLeaves));
}
/// <summary>
/// Predict a target using generalized additive models trained with the <see cref="RegressionGamTrainer"/>.
/// </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>
/// <param name="numberOfIterations">The number of iterations to use in learning the features.</param>
/// <param name="maximumBinCountPerFeature">The maximum number of bins to use to approximate features.</param>
/// <param name="learningRate">The learning rate. GAMs work best with a small learning rate.</param>
public static RegressionGamTrainer GeneralizedAdditiveModels(this RegressionCatalog.RegressionTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int numberOfIterations = GamDefaults.NumberOfIterations,
int maximumBinCountPerFeature = GamDefaults.MaximumBinCountPerFeature,
double learningRate = GamDefaults.LearningRate)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new RegressionGamTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfIterations, learningRate, maximumBinCountPerFeature));
}
/// <summary>
/// Predict a target using a decision tree regression model trained with the <see cref="FastTreeRegressionTrainer"/>.
/// </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>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
public static FastTreeRegressionTrainer FastTree(this RegressionCatalog.RegressionTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastTreeRegressionTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numLeaves, numTrees, minDatapointsInLeaves, learningRate));
}
/// <summary>
/// Predict a target using a decision tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
/// </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>
/// <param name="numberOfLeaves">The number of leaves to use.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="numberOfIterations">The number of iterations to use.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static LightGbmRegressorTrainer LightGbm(this RegressionCatalog.RegressionTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int?numberOfLeaves = null,
int?minimumExampleCountPerLeaf = null,
double?learningRate = null,
int numberOfIterations = Options.Defaults.NumberOfIterations)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new LightGbmRegressorTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations));
}
/// <summary>
/// Create <see cref="FastTreeTweedieTrainer"/>, which predicts a target using a decision tree regression model.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="labelColumnName">The name of the label column. The column data must be <see cref="System.Single"/>.</param>
/// <param name="featureColumnName">The name of the feature column. The column data must be a known-sized vector of <see cref="System.Single"/>.</param>
/// <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
/// <param name="numberOfTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="numberOfLeaves">The maximum number of leaves per decision tree.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points required to form a new tree leaf.</param>
/// <param name="learningRate">The learning rate.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionCatalog.RegressionTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string exampleWeightColumnName = null,
int numberOfLeaves = Defaults.NumberOfLeaves,
int numberOfTrees = Defaults.NumberOfTrees,
int minimumExampleCountPerLeaf = Defaults.MinimumExampleCountPerLeaf,
double learningRate = Defaults.LearningRate)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new FastTreeTweedieTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf, learningRate));
}
/// <summary>
/// Predict a target using a linear regression model trained with the SDCA trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="labelColumnName">The label column, or dependent variable.</param>
/// <param name="featureColumnName">The features, or independent variables.</param>
/// <param name="weightColumnName">The optional example weights.</param>
/// <param name="l2Const">The L2 regularization hyperparameter.</param>
/// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
/// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
/// <param name="loss">The custom loss, if unspecified will be <see cref="SquaredLoss"/>.</param>
public static SdcaRegressionTrainer StochasticDualCoordinateAscent(this RegressionCatalog.RegressionTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string weightColumnName = null,
ISupportSdcaRegressionLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new SdcaRegressionTrainer(env, labelColumnName, featureColumnName, weightColumnName, loss, l2Const, l1Threshold, maxIterations));
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.PoissonRegression"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="labelColumn">The labelColumn, or dependent variable.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="l1Weight">Weight of L1 regularization term.</param>
/// <param name="l2Weight">Weight of L2 regularization term.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
/// <param name="memorySize">Memory size for <see cref="Microsoft.ML.Trainers.PoissonRegression"/>. Low=faster, less accurate.</param>
/// <param name="enforceNoNegativity">Enforce non-negative weights.</param>
public static PoissonRegression PoissonRegression(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null,
float l1Weight = LROptions.Defaults.L1Weight,
float l2Weight = LROptions.Defaults.L2Weight,
float optimizationTolerance = LROptions.Defaults.OptTol,
int memorySize = LROptions.Defaults.MemorySize,
bool enforceNoNegativity = LROptions.Defaults.EnforceNonNegativity)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new PoissonRegression(env, labelColumn, featureColumn, weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enforceNoNegativity));
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="OnlineGradientDescentTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="labelColumn">The name of the label, or dependent variable.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="lossFunction">The custom loss. Defaults to <see cref="SquaredLoss"/> if not provided.</param>
/// <param name="learningRate">The learning Rate.</param>
/// <param name="decreaseLearningRate">Decrease learning rate as iterations progress.</param>
/// <param name="l2RegularizerWeight">L2 regularization weight.</param>
/// <param name="numIterations">Number of training iterations through the data.</param>
public static OnlineGradientDescentTrainer OnlineGradientDescent(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
IRegressionLoss lossFunction = null,
float learningRate = OnlineGradientDescentTrainer.Options.OgdDefaultArgs.LearningRate,
bool decreaseLearningRate = OnlineGradientDescentTrainer.Options.OgdDefaultArgs.DecreaseLearningRate,
float l2RegularizerWeight = AveragedLinearArguments.AveragedDefaultArgs.L2RegularizerWeight,
int numIterations = OnlineLinearArguments.OnlineDefaultArgs.NumIterations)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
return(new OnlineGradientDescentTrainer(env, labelColumn, featureColumn, learningRate, decreaseLearningRate, l2RegularizerWeight,
numIterations, lossFunction));
}
/// <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));
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="OlsLinearRegressionTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="labelColumn">The labelColumn column.</param>
/// <param name="featureColumn">The features column.</param>
/// <param name="weights">The weights column.</param>
public static OlsLinearRegressionTrainer OrdinaryLeastSquares(this RegressionCatalog.RegressionTrainers catalog,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weights = null)
{
Contracts.CheckValue(catalog, nameof(catalog));
var env = CatalogUtils.GetEnvironment(catalog);
var options = new OlsLinearRegressionTrainer.Options
{
LabelColumn = labelColumn,
FeatureColumn = featureColumn,
WeightColumn = weights != null ? Optional <string> .Explicit(weights) : Optional <string> .Implicit(DefaultColumnNames.Weight)
};
return(new OlsLinearRegressionTrainer(env, options));
}