protected CalibratorEstimatorBase(IHostEnvironment env,
TCalibratorTrainer calibratorTrainer,
IPredictor predictor = null,
string labelColumn = DefaultColumnNames.Label,
string featureColumn = DefaultColumnNames.Features,
string weightColumn = null)
{
Host = env;
Predictor = predictor;
CalibratorTrainer = calibratorTrainer;
ScoreColumn = TrainerUtils.MakeR4ScalarColumn(DefaultColumnNames.Score); // Do we fantom this being named anything else (renaming column)? Complete metadata?
LabelColumn = TrainerUtils.MakeBoolScalarLabel(labelColumn);
FeatureColumn = TrainerUtils.MakeR4VecFeature(featureColumn);
PredictedLabel = new SchemaShape.Column(DefaultColumnNames.PredictedLabel,
SchemaShape.Column.VectorKind.Scalar,
BoolType.Instance,
false,
new SchemaShape(MetadataUtils.GetTrainerOutputMetadata()));
if (weightColumn != null)
{
WeightColumn = TrainerUtils.MakeR4ScalarWeightColumn(weightColumn);
}
}
private protected LightGbmTrainerBase(IHostEnvironment env,
string name,
SchemaShape.Column labelColumn,
string featureColumnName,
string exampleWeightColumnName,
string rowGroupColumnName,
int?numberOfLeaves,
int?minimumExampleCountPerLeaf,
double?learningRate,
int numberOfIterations)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumnName),
labelColumn, TrainerUtils.MakeR4ScalarWeightColumn(exampleWeightColumnName), TrainerUtils.MakeU4ScalarColumn(rowGroupColumnName))
{
LightGbmTrainerOptions = new Options();
LightGbmTrainerOptions.NumberOfLeaves = numberOfLeaves;
LightGbmTrainerOptions.MinimumExampleCountPerLeaf = minimumExampleCountPerLeaf;
LightGbmTrainerOptions.LearningRate = learningRate;
LightGbmTrainerOptions.NumberOfIterations = numberOfIterations;
LightGbmTrainerOptions.LabelColumnName = labelColumn.Name;
LightGbmTrainerOptions.FeatureColumnName = featureColumnName;
LightGbmTrainerOptions.ExampleWeightColumnName = exampleWeightColumnName;
LightGbmTrainerOptions.RowGroupColumnName = rowGroupColumnName;
InitParallelTraining();
}
/// <summary>
/// Initializes a new instance of <see cref="OrdinaryLeastSquaresRegressionTrainer"/>
/// </summary>
internal OrdinaryLeastSquaresRegressionTrainer(IHostEnvironment env, Options options)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(options.FeatureColumnName),
TrainerUtils.MakeR4ScalarColumn(options.LabelColumnName), TrainerUtils.MakeR4ScalarWeightColumn(options.ExampleWeightColumnName))
{
Host.CheckValue(options, nameof(options));
Host.CheckUserArg(options.L2Weight >= 0, nameof(options.L2Weight), "L2 regularization term cannot be negative");
_l2Weight = options.L2Weight;
_perParameterSignificance = options.PerParameterSignificance;
}
/// <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;
}
private protected LightGbmTrainerBase(IHostEnvironment env, string name, Options options, SchemaShape.Column label)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(options.FeatureColumnName), label,
TrainerUtils.MakeR4ScalarWeightColumn(options.ExampleWeightColumnName), TrainerUtils.MakeU4ScalarColumn(options.RowGroupColumnName))
{
Host.CheckValue(options, nameof(options));
LightGbmTrainerOptions = options;
InitParallelTraining();
}
internal LbfgsTrainerBase(IHostEnvironment env,
TArgs args,
SchemaShape.Column labelColumn,
Action <TArgs> advancedSettings = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(RegisterName), TrainerUtils.MakeR4VecFeature(args.FeatureColumn),
labelColumn, TrainerUtils.MakeR4ScalarWeightColumn(args.WeightColumn, args.WeightColumn.IsExplicit))
{
Host.CheckValue(args, nameof(args));
Args = args;
// Apply the advanced args, if the user supplied any.
advancedSettings?.Invoke(args);
args.FeatureColumn = FeatureColumn.Name;
args.LabelColumn = LabelColumn.Name;
args.WeightColumn = WeightColumn?.Name;
Host.CheckUserArg(!Args.UseThreads || Args.NumThreads > 0 || Args.NumThreads == null,
nameof(Args.NumThreads), "numThreads must be positive (or empty for default)");
Host.CheckUserArg(Args.L2Weight >= 0, nameof(Args.L2Weight), "Must be non-negative");
Host.CheckUserArg(Args.L1Weight >= 0, nameof(Args.L1Weight), "Must be non-negative");
Host.CheckUserArg(Args.OptTol > 0, nameof(Args.OptTol), "Must be positive");
Host.CheckUserArg(Args.MemorySize > 0, nameof(Args.MemorySize), "Must be positive");
Host.CheckUserArg(Args.MaxIterations > 0, nameof(Args.MaxIterations), "Must be positive");
Host.CheckUserArg(Args.SgdInitializationTolerance >= 0, nameof(Args.SgdInitializationTolerance), "Must be non-negative");
Host.CheckUserArg(Args.NumThreads == null || Args.NumThreads.Value >= 0, nameof(Args.NumThreads), "Must be non-negative");
Host.CheckParam(!(Args.L2Weight < 0), nameof(Args.L2Weight), "Must be non-negative, if provided.");
Host.CheckParam(!(Args.L1Weight < 0), nameof(Args.L1Weight), "Must be non-negative, if provided");
Host.CheckParam(!(Args.OptTol <= 0), nameof(Args.OptTol), "Must be positive, if provided.");
Host.CheckParam(!(Args.MemorySize <= 0), nameof(Args.MemorySize), "Must be positive, if provided.");
L2Weight = Args.L2Weight;
L1Weight = Args.L1Weight;
OptTol = Args.OptTol;
MemorySize = Args.MemorySize;
MaxIterations = Args.MaxIterations;
SgdInitializationTolerance = Args.SgdInitializationTolerance;
Quiet = Args.Quiet;
InitWtsDiameter = Args.InitWtsDiameter;
UseThreads = Args.UseThreads;
NumThreads = Args.NumThreads;
DenseOptimizer = Args.DenseOptimizer;
EnforceNonNegativity = Args.EnforceNonNegativity;
if (EnforceNonNegativity && ShowTrainingStats)
{
ShowTrainingStats = false;
using (var ch = Host.Start("Initialization"))
{
ch.Warning("The training statistics cannot be computed with non-negativity constraint.");
}
}
ShowTrainingStats = false;
_srcPredictor = default;
}
internal LbfgsTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.Column labelColumn,
float?l1Weight = null,
float?l2Weight = null,
float?optimizationTolerance = null,
int?memorySize = null,
bool?enforceNoNegativity = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(RegisterName), TrainerUtils.MakeR4VecFeature(args.FeatureColumn),
labelColumn, TrainerUtils.MakeR4ScalarWeightColumn(args.WeightColumn))
{
Host.CheckValue(args, nameof(args));
Args = args;
Host.CheckUserArg(!Args.UseThreads || Args.NumThreads > 0 || Args.NumThreads == null,
nameof(Args.NumThreads), "numThreads must be positive (or empty for default)");
Host.CheckUserArg(Args.L2Weight >= 0, nameof(Args.L2Weight), "Must be non-negative");
Host.CheckUserArg(Args.L1Weight >= 0, nameof(Args.L1Weight), "Must be non-negative");
Host.CheckUserArg(Args.OptTol > 0, nameof(Args.OptTol), "Must be positive");
Host.CheckUserArg(Args.MemorySize > 0, nameof(Args.MemorySize), "Must be positive");
Host.CheckUserArg(Args.MaxIterations > 0, nameof(Args.MaxIterations), "Must be positive");
Host.CheckUserArg(Args.SgdInitializationTolerance >= 0, nameof(Args.SgdInitializationTolerance), "Must be non-negative");
Host.CheckUserArg(Args.NumThreads == null || Args.NumThreads.Value >= 0, nameof(Args.NumThreads), "Must be non-negative");
Host.CheckParam(!(l2Weight < 0), nameof(l2Weight), "Must be non-negative, if provided.");
Host.CheckParam(!(l1Weight < 0), nameof(l1Weight), "Must be non-negative, if provided");
Host.CheckParam(!(optimizationTolerance <= 0), nameof(optimizationTolerance), "Must be positive, if provided.");
Host.CheckParam(!(memorySize <= 0), nameof(memorySize), "Must be positive, if provided.");
// Review: Warn about the overriding behavior
L2Weight = l2Weight ?? Args.L2Weight;
L1Weight = l1Weight ?? Args.L1Weight;
OptTol = optimizationTolerance ?? Args.OptTol;
MemorySize = memorySize ?? Args.MemorySize;
MaxIterations = Args.MaxIterations;
SgdInitializationTolerance = Args.SgdInitializationTolerance;
Quiet = Args.Quiet;
InitWtsDiameter = Args.InitWtsDiameter;
UseThreads = Args.UseThreads;
NumThreads = Args.NumThreads;
DenseOptimizer = Args.DenseOptimizer;
EnforceNonNegativity = enforceNoNegativity ?? Args.EnforceNonNegativity;
if (EnforceNonNegativity && ShowTrainingStats)
{
ShowTrainingStats = false;
using (var ch = Host.Start("Initialization"))
{
ch.Warning("The training statistics cannot be computed with non-negativity constraint.");
ch.Done();
}
}
ShowTrainingStats = false;
_srcPredictor = default;
}
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="SdcaMultiClassTrainer"/>
/// </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 SdcaMultiClassTrainer(IHostEnvironment env,
string featureColumn,
string labelColumn,
string weightColumn = null,
ISupportSdcaClassificationLoss loss = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <Arguments> advancedSettings = null)
: base(env, featureColumn, TrainerUtils.MakeU4ScalarLabel(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;
}
private protected CalibratorEstimatorBase(IHostEnvironment env,
ICalibratorTrainer calibratorTrainer, string labelColumn, string scoreColumn, string weightColumn)
{
Host = env;
_calibratorTrainer = calibratorTrainer;
if (!string.IsNullOrWhiteSpace(labelColumn))
{
LabelColumn = TrainerUtils.MakeBoolScalarLabel(labelColumn);
}
else
{
env.CheckParam(!calibratorTrainer.NeedsTraining, nameof(labelColumn), "For trained calibrators, " + nameof(labelColumn) + " must be specified.");
}
ScoreColumn = TrainerUtils.MakeR4ScalarColumn(scoreColumn); // Do we fanthom this being named anything else (renaming column)? Complete metadata?
if (weightColumn != null)
{
WeightColumn = TrainerUtils.MakeR4ScalarWeightColumn(weightColumn);
}
}
private protected GamTrainerBase(IHostEnvironment env, TArgs args, string name, SchemaShape.Column label)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(args.FeatureColumn),
label, TrainerUtils.MakeR4ScalarWeightColumn(args.WeightColumn))
{
Contracts.CheckValue(env, nameof(env));
Host.CheckValue(args, nameof(args));
Host.CheckParam(args.LearningRates > 0, nameof(args.LearningRates), "Must be positive.");
Host.CheckParam(args.NumThreads == null || args.NumThreads > 0, nameof(args.NumThreads), "Must be positive.");
Host.CheckParam(0 <= args.EntropyCoefficient && args.EntropyCoefficient <= 1, nameof(args.EntropyCoefficient), "Must be in [0, 1].");
Host.CheckParam(0 <= args.GainConfidenceLevel && args.GainConfidenceLevel < 1, nameof(args.GainConfidenceLevel), "Must be in [0, 1).");
Host.CheckParam(0 < args.MaxBins, nameof(args.MaxBins), "Must be posittive.");
Host.CheckParam(0 < args.NumIterations, nameof(args.NumIterations), "Must be positive.");
Host.CheckParam(0 < args.MinDocuments, nameof(args.MinDocuments), "Must be positive.");
Args = args;
Info = new TrainerInfo(normalization: false, calibration: NeedCalibration, caching: false, supportValid: true);
_gainConfidenceInSquaredStandardDeviations = Math.Pow(ProbabilityFunctions.Probit(1 - (1 - Args.GainConfidenceLevel) * 0.5), 2);
_entropyCoefficient = Args.EntropyCoefficient * 1e-6;
InitializeThreads();
}
private protected GamTrainerBase(IHostEnvironment env, TOptions options, string name, SchemaShape.Column label)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(options.FeatureColumnName),
label, TrainerUtils.MakeR4ScalarWeightColumn(options.ExampleWeightColumnName))
{
Contracts.CheckValue(env, nameof(env));
Host.CheckValue(options, nameof(options));
Host.CheckParam(options.LearningRate > 0, nameof(options.LearningRate), "Must be positive.");
Host.CheckParam(options.NumberOfThreads == null || options.NumberOfThreads > 0, nameof(options.NumberOfThreads), "Must be positive.");
Host.CheckParam(0 <= options.EntropyCoefficient && options.EntropyCoefficient <= 1, nameof(options.EntropyCoefficient), "Must be in [0, 1].");
Host.CheckParam(0 <= options.GainConfidenceLevel && options.GainConfidenceLevel < 1, nameof(options.GainConfidenceLevel), "Must be in [0, 1).");
Host.CheckParam(0 < options.MaximumBinCountPerFeature, nameof(options.MaximumBinCountPerFeature), "Must be positive.");
Host.CheckParam(0 < options.NumberOfIterations, nameof(options.NumberOfIterations), "Must be positive.");
Host.CheckParam(0 < options.MinimumExampleCountPerLeaf, nameof(options.MinimumExampleCountPerLeaf), "Must be positive.");
GamTrainerOptions = options;
Info = new TrainerInfo(normalization: false, calibration: NeedCalibration, caching: false, supportValid: true);
_gainConfidenceInSquaredStandardDeviations = Math.Pow(ProbabilityFunctions.Probit(1 - (1 - GamTrainerOptions.GainConfidenceLevel) * 0.5), 2);
_entropyCoefficient = GamTrainerOptions.EntropyCoefficient * 1e-6;
InitializeThreads();
}
private RandomizedPcaTrainer(IHostEnvironment env, Options options, string featureColumn, string weightColumn,
int rank = 20, int oversampling = 20, bool center = true, int?seed = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(featureColumn), default, TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
{
// if the args are not null, we got here from maml, and the internal ctor.
if (options != null)
{
_rank = options.Rank;
_center = options.Center;
_oversampling = options.Oversampling;
_seed = options.Seed ?? Host.Rand.Next();
}
else
{
_rank = rank;
_center = center;
_oversampling = oversampling;
_seed = seed ?? Host.Rand.Next();
}
_featureColumn = featureColumn;
Host.CheckUserArg(_rank > 0, nameof(_rank), "Rank must be positive");
Host.CheckUserArg(_oversampling >= 0, nameof(_oversampling), "Oversampling must be non-negative");
}
protected OnlineLinearTrainer(OnlineLinearArguments args, IHostEnvironment env, string name, SchemaShape.Column label)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(args.FeatureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(args.InitialWeights))
{
Contracts.CheckValue(args, nameof(args));
Contracts.CheckUserArg(args.NumIterations > 0, nameof(args.NumIterations), UserErrorPositive);
Contracts.CheckUserArg(args.InitWtsDiameter >= 0, nameof(args.InitWtsDiameter), UserErrorNonNegative);
Contracts.CheckUserArg(args.StreamingCacheSize > 0, nameof(args.StreamingCacheSize), UserErrorPositive);
Args = args;
Name = name;
// REVIEW: Caching could be false for one iteration, if we got around the whole shuffling issue.
Info = new TrainerInfo(calibration: NeedCalibration, supportIncrementalTrain: true);
}
private protected GamTrainerBase(IHostEnvironment env,
string name,
SchemaShape.Column label,
string featureColumnName,
string weightCrowGroupColumnName,
int numberOfIterations,
double learningRate,
int maximumBinCountPerFeature)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumnName), label, TrainerUtils.MakeR4ScalarWeightColumn(weightCrowGroupColumnName))
{
GamTrainerOptions = new TOptions();
GamTrainerOptions.NumberOfIterations = numberOfIterations;
GamTrainerOptions.LearningRate = learningRate;
GamTrainerOptions.MaximumBinCountPerFeature = maximumBinCountPerFeature;
GamTrainerOptions.LabelColumnName = label.Name;
GamTrainerOptions.FeatureColumnName = featureColumnName;
if (weightCrowGroupColumnName != null)
{
GamTrainerOptions.ExampleWeightColumnName = weightCrowGroupColumnName;
}
Info = new TrainerInfo(normalization: false, calibration: NeedCalibration, caching: false, supportValid: true);
_gainConfidenceInSquaredStandardDeviations = Math.Pow(ProbabilityFunctions.Probit(1 - (1 - GamTrainerOptions.GainConfidenceLevel) * 0.5), 2);
_entropyCoefficient = GamTrainerOptions.EntropyCoefficient * 1e-6;
InitializeThreads();
}
private protected LightGbmTrainerBase(IHostEnvironment env,
string name,
SchemaShape.Column label,
string featureColumn,
string weightColumn,
string groupIdColumn,
int?numLeaves,
int?minDataPerLeaf,
double?learningRate,
int numBoostRound)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(weightColumn), TrainerUtils.MakeU4ScalarColumn(groupIdColumn))
{
Args = new Options();
Args.NumLeaves = numLeaves;
Args.MinDataPerLeaf = minDataPerLeaf;
Args.LearningRate = learningRate;
Args.NumBoostRound = numBoostRound;
Args.LabelColumn = label.Name;
Args.FeatureColumn = featureColumn;
if (weightColumn != null)
{
Args.WeightColumn = Optional <string> .Explicit(weightColumn);
}
if (groupIdColumn != null)
{
Args.GroupIdColumn = Optional <string> .Explicit(groupIdColumn);
}
InitParallelTraining();
}
private protected LightGbmTrainerBase(IHostEnvironment env,
string name,
SchemaShape.Column label,
string featureColumn,
string weightColumn,
string groupIdColumn,
int?numLeaves,
int?minDataPerLeaf,
double?learningRate,
int numBoostRound)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(weightColumn), TrainerUtils.MakeU4ScalarColumn(groupIdColumn))
{
LightGbmTrainerOptions = new Options();
LightGbmTrainerOptions.NumLeaves = numLeaves;
LightGbmTrainerOptions.MinDataPerLeaf = minDataPerLeaf;
LightGbmTrainerOptions.LearningRate = learningRate;
LightGbmTrainerOptions.NumBoostRound = numBoostRound;
LightGbmTrainerOptions.LabelColumnName = label.Name;
LightGbmTrainerOptions.FeatureColumnName = featureColumn;
LightGbmTrainerOptions.ExampleWeightColumnName = weightColumn;
LightGbmTrainerOptions.RowGroupColumnName = groupIdColumn;
InitParallelTraining();
}
private protected GamTrainerBase(IHostEnvironment env,
string name,
SchemaShape.Column label,
string featureColumn,
string weightColumn,
int numIterations,
double learningRate,
int maxBins)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
{
Args = new TArgs();
Args.NumIterations = numIterations;
Args.LearningRates = learningRate;
Args.MaxBins = maxBins;
Args.LabelColumn = label.Name;
Args.FeatureColumn = featureColumn;
if (weightColumn != null)
{
Args.WeightColumn = weightColumn;
}
Info = new TrainerInfo(normalization: false, calibration: NeedCalibration, caching: false, supportValid: true);
_gainConfidenceInSquaredStandardDeviations = Math.Pow(ProbabilityFunctions.Probit(1 - (1 - Args.GainConfidenceLevel) * 0.5), 2);
_entropyCoefficient = Args.EntropyCoefficient * 1e-6;
InitializeThreads();
}
private protected LightGbmTrainerBase(IHostEnvironment env, string name, LightGbmArguments args, SchemaShape.Column label)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(args.FeatureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(args.WeightColumn))
{
Host.CheckValue(args, nameof(args));
Args = args;
InitParallelTraining();
}
private protected LightGbmTrainerBase(IHostEnvironment env, string name, SchemaShape.Column label, string featureColumn,
string weightColumn = null, string groupIdColumn = null, Action <LightGbmArguments> advancedSettings = null)
: base(Contracts.CheckRef(env, nameof(env)).Register(name), TrainerUtils.MakeR4VecFeature(featureColumn), label, TrainerUtils.MakeR4ScalarWeightColumn(weightColumn))
{
Args = new LightGbmArguments();
//apply the advanced args, if the user supplied any
advancedSettings?.Invoke(Args);
// check that the users didn't specify different label, group, feature, weights in the args, from what they supplied directly
TrainerUtils.CheckArgsHaveDefaultColNames(Host, Args);
Args.LabelColumn = label.Name;
Args.FeatureColumn = featureColumn;
if (weightColumn != null)
{
Args.WeightColumn = weightColumn;
}
if (groupIdColumn != null)
{
Args.GroupIdColumn = groupIdColumn;
}
InitParallelTraining();
}