/// <summary>
/// Initializes a new instance of <see cref="PoissonRegression"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the example weight column.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public PoissonRegression(IHostEnvironment env, string featureColumn, string labelColumn,
string weightColumn = null, Action <Arguments> advancedSettings = null)
: base(env, featureColumn, TrainerUtils.MakeR4ScalarLabel(labelColumn), weightColumn, advancedSettings)
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
}
/// <summary>
/// Initializes a new instance of <see cref="LightGbmRegressorTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="groupIdColumn">The name for the column containing the group ID. </param>
/// <param name="weightColumn">The name for the column containing the initial weight.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public LightGbmRegressorTrainer(IHostEnvironment env, string labelColumn, string featureColumn,
string groupIdColumn = null, string weightColumn = null, Action <LightGbmArguments> advancedSettings = null)
: base(env, LoadNameValue, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weightColumn, groupIdColumn, advancedSettings)
{
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
}
/// <summary>
/// Initializes a new instance of <see cref="FastTreeTweedieTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="groupIdColumn">The name for the column containing the group ID. </param>
/// <param name="weightColumn">The name for the column containing the initial weight.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public FastTreeTweedieTrainer(IHostEnvironment env, string labelColumn, string featureColumn,
string groupIdColumn = null, string weightColumn = null, Action <Arguments> advancedSettings = null)
: base(env, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weightColumn, groupIdColumn, advancedSettings)
{
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Initialize();
}
/// <summary>
/// Initializes a new instance of <see cref="OlsLinearRegressionTrainer"/>
/// </summary>
internal OlsLinearRegressionTrainer(IHostEnvironment env, Arguments args)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(args.FeatureColumn),
TrainerUtils.MakeR4ScalarLabel(args.LabelColumn), TrainerUtils.MakeR4ScalarWeightColumn(args.WeightColumn, args.WeightColumn.IsExplicit))
{
Host.CheckValue(args, nameof(args));
Host.CheckUserArg(args.L2Weight >= 0, nameof(args.L2Weight), "L2 regularization term cannot be negative");
_l2Weight = args.L2Weight;
_perParameterSignificance = args.PerParameterSignificance;
}
internal SdcaRegressionTrainer(IHostEnvironment env, Arguments args, string featureColumn, string labelColumn, string weightColumn = null)
: base(env, args, TrainerUtils.MakeR4ScalarLabel(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
{
Host.CheckValue(labelColumn, nameof(labelColumn));
Host.CheckValue(featureColumn, nameof(featureColumn));
_loss = args.LossFunction.CreateComponent(env);
Loss = _loss;
}
/// <summary>
/// Initializes a new instance of <see cref="LogisticRegression"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the example weight column.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public LogisticRegression(IHostEnvironment env, string featureColumn, string labelColumn,
string weightColumn = null, Action <Arguments> advancedSettings = null)
: base(env, featureColumn, TrainerUtils.MakeR4ScalarLabel(labelColumn), weightColumn, advancedSettings)
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
_posWeight = 0;
ShowTrainingStats = Args.ShowTrainingStats;
}
/// <summary>
/// Initializes a new instance of <see cref="FastTreeBinaryClassificationTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the column containing the initial weight.</param>
/// <param name="minDocumentsInLeafs">The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public RegressionGamTrainer(IHostEnvironment env,
string labelColumn,
string featureColumn,
string weightColumn = null,
int minDocumentsInLeafs = Defaults.MinDocumentsInLeafs,
double learningRate = Defaults.LearningRates,
Action <Arguments> advancedSettings = null)
: base(env, LoadNameValue, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weightColumn, minDocumentsInLeafs, learningRate, advancedSettings)
{
}
/// <summary>
/// Initializes a new instance of <see cref="LightGbmRegressorTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the column containing the initial weight.</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>
/// <param name="advancedSettings">A delegate to set more settings.
/// The settings here will override the ones provided in the direct signature,
/// if both are present and have different values.
/// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
public LightGbmRegressorTrainer(IHostEnvironment env, string labelColumn, string featureColumn,
string weightColumn = null,
int?numLeaves = null,
int?minDataPerLeaf = null,
double?learningRate = null,
int numBoostRound = LightGbmArguments.Defaults.NumBoostRound,
Action <LightGbmArguments> advancedSettings = null)
: base(env, LoadNameValue, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weightColumn, null, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings)
{
}
public OnlineGradientDescentTrainer(IHostEnvironment env, Arguments args)
: base(args, env, UserNameValue, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn))
{
LossFunction = args.LossFunction.CreateComponent(env);
_outputColumns = new[]
{
new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata()))
};
}
public SdcaRegressionTrainer(IHostEnvironment env, Arguments args, string featureColumn, string labelColumn, string weightColumn = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), args, TrainerUtils.MakeR4VecFeature(featureColumn),
TrainerUtils.MakeR4ScalarLabel(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
{
Host.CheckValue(labelColumn, nameof(labelColumn));
Host.CheckValue(featureColumn, nameof(featureColumn));
_loss = args.LossFunction.CreateComponent(env);
Loss = _loss;
_args = args;
}
/// <summary>
/// Initializes a new instance of <see cref="FastTreeBinaryClassificationTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the column containing the initial weight.</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>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public RegressionGamTrainer(IHostEnvironment env,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weightColumn = null,
int numIterations = GamDefaults.NumIterations,
double learningRate = GamDefaults.LearningRates,
int maxBins = GamDefaults.MaxBins,
Action <Arguments> advancedSettings = null)
: base(env, LoadNameValue, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weightColumn, numIterations, learningRate, maxBins, advancedSettings)
{
}
/// <summary>
/// Initializes a new instance of <see cref="FastTreeRegressionTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the column containing the initial weight.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="minDocumentsInLeafs">The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="numLeaves">The max number of leaves in each regression tree.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public FastTreeRegressionTrainer(IHostEnvironment env,
string labelColumn,
string featureColumn,
string weightColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDocumentsInLeafs = Defaults.MinDocumentsInLeafs,
double learningRate = Defaults.LearningRates,
Action <Arguments> advancedSettings = null)
: base(env, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weightColumn, null, numLeaves, numTrees, minDocumentsInLeafs, learningRate, advancedSettings)
{
}
/// <summary>
/// Initializes a new instance of <see cref="LightGbmRankingTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="groupId">The name of the column containing the group ID. </param>
/// <param name="weights">The name of the optional column containing the initial weights.</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>
/// <param name="advancedSettings">A delegate to set more settings.
/// The settings here will override the ones provided in the direct signature,
/// if both are present and have different values.
/// The columns names, however need to be provided directly, not through the <paramref name="advancedSettings"/>.</param>
public LightGbmRankingTrainer(IHostEnvironment env,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string groupId = DefaultColumnNames.GroupId,
string weights = null,
int?numLeaves = null,
int?minDataPerLeaf = null,
double?learningRate = null,
int numBoostRound = LightGbmArguments.Defaults.NumBoostRound,
Action <LightGbmArguments> advancedSettings = null)
: base(env, LoadNameValue, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weights, groupId, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings)
{
Host.CheckNonEmpty(groupId, nameof(groupId));
}
/// <summary>
/// Initializes a new instance of <see cref="PoissonRegression"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the example weight column.</param>
/// <param name="enforceNoNegativity">Enforce non-negative weights.</param>
/// <param name="l1Weight">Weight of L1 regularizer term.</param>
/// <param name="l2Weight">Weight of L2 regularizer term.</param>
/// <param name="memorySize">Memory size for <see cref="LogisticRegression"/>. Lower=faster, less accurate.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public PoissonRegression(IHostEnvironment env, string featureColumn, string labelColumn,
string weightColumn = null,
float l1Weight = Arguments.Defaults.L1Weight,
float l2Weight = Arguments.Defaults.L2Weight,
float optimizationTolerance = Arguments.Defaults.OptTol,
int memorySize = Arguments.Defaults.MemorySize,
bool enforceNoNegativity = Arguments.Defaults.EnforceNonNegativity,
Action <Arguments> advancedSettings = null)
: base(env, featureColumn, TrainerUtils.MakeR4ScalarLabel(labelColumn), weightColumn, advancedSettings,
l1Weight, l2Weight, optimizationTolerance, memorySize, enforceNoNegativity)
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
}
/// <summary>
/// Initializes a new instance of <see cref="FastTreeTweedieTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the column containing the initial weight.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="minDatapointsInLeaves">The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="numLeaves">The max number of leaves in each regression tree.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
public FastTreeTweedieTrainer(IHostEnvironment env,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weightColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
double learningRate = Defaults.LearningRates,
Action <Arguments> advancedSettings = null)
: base(env, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weightColumn, null, numLeaves, numTrees, minDatapointsInLeaves, learningRate, advancedSettings)
{
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Initialize();
}
/// <summary>
/// Initializes a new instance of <see cref="SdcaRegressionTrainer"/>
/// </summary>
/// <param name="env">The environment to use.</param>
/// <param name="featureColumn">The features, or independent variables.</param>
/// <param name="labelColumn">The label, or dependent variable.</param>
/// <param name="loss">The custom loss.</param>
/// <param name="weightColumn">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="advancedSettings">A delegate to set more settings.</param>
public SdcaRegressionTrainer(IHostEnvironment env,
string featureColumn,
string labelColumn,
string weightColumn = null,
ISupportSdcaRegressionLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <Arguments> advancedSettings = null)
: base(env, featureColumn, TrainerUtils.MakeR4ScalarLabel(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weightColumn), advancedSettings,
l2Const, l1Threshold, maxIterations)
{
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
_loss = loss ?? Args.LossFunction.CreateComponent(env);
Loss = _loss;
}
/// <summary>
/// Initializes a new instance of <see cref="FastTreeRegressionTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the column containing the initial weight.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="minDocumentsInLeafs">The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data.</param>
/// <param name="numLeaves">The max number of leaves in each regression tree.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
public FastTreeRegressionTrainer(IHostEnvironment env,
string labelColumn,
string featureColumn,
string weightColumn = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDocumentsInLeafs = Defaults.MinDocumentsInLeafs,
double learningRate = Defaults.LearningRates,
Action <Arguments> advancedSettings = null)
: base(env, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weightColumn, null, advancedSettings)
{
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
if (advancedSettings != null)
{
CheckArgsAndAdvancedSettingMismatch(numLeaves, numTrees, minDocumentsInLeafs, learningRate, new Arguments(), Args);
}
}
/// <summary>
/// Trains a new <see cref="RegressionPredictionTransformer{LinearRegressionPredictor}"/>.
/// </summary>
/// <param name="env">The pricate instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">Name of the label column.</param>
/// <param name="featureColumn">Name of the feature column.</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>
/// <param name="weightsColumn">The name of the weights column.</param>
/// <param name="lossFunction">The custom loss functions. Defaults to <see cref="SquaredLoss"/> if not provided.</param>
public OnlineGradientDescentTrainer(IHostEnvironment env,
string labelColumn,
string featureColumn,
float learningRate = Arguments.OgdDefaultArgs.LearningRate,
bool decreaseLearningRate = Arguments.OgdDefaultArgs.DecreaseLearningRate,
float l2RegularizerWeight = Arguments.OgdDefaultArgs.L2RegularizerWeight,
int numIterations = Arguments.OgdDefaultArgs.NumIterations,
string weightsColumn = null,
IRegressionLoss lossFunction = null)
: base(new Arguments
{
LearningRate = learningRate,
DecreaseLearningRate = decreaseLearningRate,
L2RegularizerWeight = l2RegularizerWeight,
NumIterations = numIterations,
LabelColumn = labelColumn,
FeatureColumn = featureColumn,
InitialWeights = weightsColumn
}, env, UserNameValue, TrainerUtils.MakeR4ScalarLabel(labelColumn))
{
LossFunction = lossFunction ?? new SquaredLoss();
}
/// <summary>
/// Initializes a new instance of <see cref="LightGbmRegressorTrainer"/>
/// </summary>
/// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
/// <param name="labelColumn">The name of the label column.</param>
/// <param name="featureColumn">The name of the feature column.</param>
/// <param name="weightColumn">The name for the column containing the initial weight.</param>
/// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</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>
public LightGbmRegressorTrainer(IHostEnvironment env, string labelColumn, string featureColumn,
string weightColumn = null,
int?numLeaves = null,
int?minDataPerLeaf = null,
double?learningRate = null,
int numBoostRound = LightGbmArguments.Defaults.NumBoostRound,
Action <LightGbmArguments> advancedSettings = null)
: base(env, LoadNameValue, TrainerUtils.MakeR4ScalarLabel(labelColumn), featureColumn, weightColumn, null, advancedSettings)
{
Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
if (advancedSettings != null)
{
CheckArgsAndAdvancedSettingMismatch(numLeaves, minDataPerLeaf, learningRate, numBoostRound, new LightGbmArguments(), Args);
}
// override with the directly provided values
Args.NumBoostRound = numBoostRound;
Args.NumLeaves = numLeaves ?? Args.NumLeaves;
Args.LearningRate = learningRate ?? Args.LearningRate;
Args.MinDataPerLeaf = minDataPerLeaf ?? Args.MinDataPerLeaf;
}
internal OnlineGradientDescentTrainer(IHostEnvironment env, Arguments args)
: base(args, env, UserNameValue, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn))
{
LossFunction = args.LossFunction.CreateComponent(env);
}
/// <summary>
/// Initializes a new instance of <see cref="FastTreeTweedieTrainer"/> by using the legacy <see cref="Arguments"/> class.
/// </summary>
internal FastTreeTweedieTrainer(IHostEnvironment env, Arguments args)
: base(env, args, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn))
{
Initialize();
}
internal RegressionGamTrainer(IHostEnvironment env, Arguments args)
: base(env, args, LoadNameValue, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn))
{
}
/// <summary>
/// Initializes a new instance of <see cref="LogisticRegression"/>
/// </summary>
internal LogisticRegression(IHostEnvironment env, Arguments args)
: base(env, args, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn))
{
_posWeight = 0;
ShowTrainingStats = Args.ShowTrainingStats;
}
/// <summary>
/// Initializes a new instance of <see cref="FastTreeRegressionTrainer"/> by using the legacy <see cref="Arguments"/> class.
/// </summary>
internal FastTreeRegressionTrainer(IHostEnvironment env, Arguments args)
: base(env, args, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn))
{
}
internal LightGbmRegressorTrainer(IHostEnvironment env, LightGbmArguments args)
: base(env, LoadNameValue, args, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn))
{
}
/// <summary>
/// Initializes a new instance of <see cref="FastForestRegression"/> by using the legacy <see cref="Arguments"/> class.
/// </summary>
public FastForestRegression(IHostEnvironment env, Arguments args)
: base(env, args, TrainerUtils.MakeR4ScalarLabel(args.LabelColumn), true)
{
}