/// <summary> /// Initializes a new instance of <see cref="SdcaRegressionTrainer"/> /// </summary> /// <param name="env">The environment to use.</param> /// <param name="labelColumn">The label, or dependent variable.</param> /// <param name="featureColumn">The features, or independent variables.</param> /// <param name="weights">The optional example weights.</param> /// <param name="loss">The custom loss.</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> internal SdcaRegressionTrainer(IHostEnvironment env, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weights = null, ISupportSdcaRegressionLoss loss = null, float?l2Const = null, float?l1Threshold = null, int?maxIterations = null) : base(env, featureColumn, TrainerUtils.MakeR4ScalarColumn(labelColumn), TrainerUtils.MakeR4ScalarWeightColumn(weights), l2Const, l1Threshold, maxIterations) { Host.CheckNonEmpty(featureColumn, nameof(featureColumn)); Host.CheckNonEmpty(labelColumn, nameof(labelColumn)); _loss = loss ?? SdcaTrainerOptions.LossFunction.CreateComponent(env); Loss = _loss; }
/// <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. /// The settings here will override the ones provided in the direct method 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 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> /// Predict a target using a linear regression model trained with the SDCA trainer. /// </summary> /// <param name="ctx">The regression context trainer object.</param> /// <param name="label">The label, or dependent variable.</param> /// <param name="features">The features, or independent variables.</param> /// <param name="weights">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="SquaredLossSDCARegressionLossFunction"/>.</param> /// <param name="onFit">A delegate that is called every time the /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the /// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}"/> instance created out of this. This delegate will receive /// the linear model that was trained. Note that this action cannot change the result in any way; it is only a way for the caller to /// be informed about what was learnt.</param> /// <returns>The predicted output.</returns> public static Scalar <float> Sdca(this RegressionContext.RegressionTrainers ctx, Scalar <float> label, Vector <float> features, Scalar <float> weights = null, float?l2Const = null, float?l1Threshold = null, int?maxIterations = null, ISupportSdcaRegressionLoss loss = null, Action <LinearRegressionPredictor> onFit = null) { Contracts.CheckValue(label, nameof(label)); Contracts.CheckValue(features, nameof(features)); Contracts.CheckValueOrNull(weights); Contracts.CheckParam(!(l2Const < 0), nameof(l2Const), "Must not be negative, if specified."); Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified."); Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified"); Contracts.CheckValueOrNull(loss); Contracts.CheckValueOrNull(onFit); var args = new SdcaRegressionTrainer.Arguments() { L2Const = l2Const, L1Threshold = l1Threshold, MaxIterations = maxIterations }; if (loss != null) { args.LossFunction = new TrivialRegressionLossFactory(loss); } var rec = new TrainerEstimatorReconciler.Regression( (env, labelName, featuresName, weightsName) => { var trainer = new SdcaRegressionTrainer(env, args, featuresName, labelName, weightsName); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, weights); return(rec.Score); }
public TrivialRegressionLossFactory(ISupportSdcaRegressionLoss loss) { _loss = loss; }