/// <summary> /// FastTree <see cref="RegressionCatalog"/> extension method. /// Predicts 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="label">The label column.</param> /// <param name="features">The features column.</param> /// <param name="weights">The optional weights column.</param> /// <param name="options">Algorithm advanced settings.</param> /// <param name="onFit">A delegate that is called every time the /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the /// <see cref="Estimator{TInShape, TOutShape, 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 Score output column indicating the predicted value.</returns> /// <example> /// <format type="text/markdown"> /// <![CDATA[ /// [!code-csharp[FastTree](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs)] /// ]]></format> /// </example> public static Scalar <float> FastTree(this RegressionCatalog.RegressionTrainers catalog, Scalar <float> label, Vector <float> features, Scalar <float> weights, FastTreeRegressionTrainer.Options options, Action <FastTreeRegressionModelParameters> onFit = null) { Contracts.CheckValueOrNull(options); CheckUserValues(label, features, weights, onFit); var rec = new TrainerEstimatorReconciler.Regression( (env, labelName, featuresName, weightsName) => { options.LabelColumn = labelName; options.FeatureColumn = featuresName; options.WeightColumn = weightsName; var trainer = new FastTreeRegressionTrainer(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, weights); return(rec.Score); }
/// <summary> /// FastTree <see cref="RegressionContext"/> extension method. /// Predicts a target using a decision tree regression model trained with the <see cref="FastTreeRegressionTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="RegressionContext"/>.</param> /// <param name="label">The label column.</param> /// <param name="features">The features colum.</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="minDatapointsInLeafs">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> /// <param name="advancedSettings">Algorithm advanced settings.</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 Score output column indicating the predicted value.</returns> public static Scalar <float> FastTree(this RegressionContext.RegressionTrainers ctx, Scalar <float> label, Vector <float> features, Scalar <float> weights = null, int numLeaves = Defaults.NumLeaves, int numTrees = Defaults.NumTrees, int minDatapointsInLeafs = Defaults.MinDocumentsInLeafs, double learningRate = Defaults.LearningRates, Action <FastTreeRegressionTrainer.Arguments> advancedSettings = null, Action <FastTreeRegressionPredictor> onFit = null) { FastTreeStaticsUtils.CheckUserValues(label, features, weights, numLeaves, numTrees, minDatapointsInLeafs, learningRate, advancedSettings, onFit); var rec = new TrainerEstimatorReconciler.Regression( (env, labelName, featuresName, weightsName) => { var trainer = new FastTreeRegressionTrainer(env, labelName, featuresName, weightsName, numLeaves, numTrees, minDatapointsInLeafs, learningRate, advancedSettings); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, weights); return(rec.Score); }
/// <summary> /// FastTree <see cref="RegressionCatalog"/> extension method. /// Predicts 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="label">The label column.</param> /// <param name="features">The features column.</param> /// <param name="weights">The optional weights column.</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 allowed in a leaf of a regression tree, out of the subsampled data.</param> /// <param name="learningRate">The learning rate.</param> /// <param name="onFit">A delegate that is called every time the /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the /// <see cref="Estimator{TInShape, TOutShape, 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 Score output column indicating the predicted value.</returns> /// <example> /// <format type="text/markdown"> /// <![CDATA[ /// [!code-csharp[FastTree](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs)] /// ]]></format> /// </example> public static Scalar <float> FastTree(this RegressionCatalog.RegressionTrainers catalog, Scalar <float> label, Vector <float> features, Scalar <float> weights = null, int numberOfLeaves = Defaults.NumberOfLeaves, int numberOfTrees = Defaults.NumberOfTrees, int minimumExampleCountPerLeaf = Defaults.MinimumExampleCountPerLeaf, double learningRate = Defaults.LearningRate, Action <FastTreeRegressionModelParameters> onFit = null) { CheckUserValues(label, features, weights, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf, learningRate, onFit); var rec = new TrainerEstimatorReconciler.Regression( (env, labelName, featuresName, weightsName) => { var trainer = new FastTreeRegressionTrainer(env, labelName, featuresName, weightsName, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf, learningRate); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, weights); return(rec.Score); }