/// <summary> /// Predict a target using a decision tree binary classification model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param> /// <param name="options">Algorithm advanced settings.</param> public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassificationContext.BinaryClassificationTrainers ctx, FastTreeBinaryClassificationTrainer.Options options) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new FastTreeBinaryClassificationTrainer(env, options)); }
/// <summary> /// FastTree <see cref="BinaryClassificationCatalog"/> extension method. /// Predict a target using a decision tree binary classification model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>. /// </summary> /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</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 set of output columns including in order the predicted binary classification score (which will range /// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns> /// <example> /// <format type="text/markdown"> /// <![CDATA[ /// [!code-csharp[FastTree](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs)] /// ]]></format> /// </example> public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) FastTree(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Scalar <bool> label, Vector <float> features, Scalar <float> weights, FastTreeBinaryClassificationTrainer.Options options, Action <CalibratedModelParametersBase <FastTreeBinaryModelParameters, PlattCalibrator> > onFit = null) { Contracts.CheckValueOrNull(options); CheckUserValues(label, features, weights, onFit); var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { options.LabelColumnName = labelName; options.FeatureColumnName = featuresName; options.ExampleWeightColumnName = weightsName; var trainer = new FastTreeBinaryClassificationTrainer(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } else { return(trainer); } }, label, features, weights); return(rec.Output); }
/// <summary> /// FastTree <see cref="BinaryClassificationCatalog"/> extension method. /// Predict a target using a decision tree binary classificaiton model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>. /// </summary> /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</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 set of output columns including in order the predicted binary classification score (which will range /// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns> /// <example> /// <format type="text/markdown"> /// <![CDATA[ /// [!code-csharp[FastTree](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs)] /// ]]></format> /// </example> public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) FastTree(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, Scalar <bool> label, Vector <float> features, Scalar <float> weights, FastTreeBinaryClassificationTrainer.Options options, Action <IPredictorWithFeatureWeights <float> > onFit = null) { Contracts.CheckValueOrNull(options); CheckUserValues(label, features, weights, onFit); var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { options.LabelColumn = labelName; options.FeatureColumn = featuresName; options.WeightColumn = weightsName != null ? Optional <string> .Explicit(weightsName) : Optional <string> .Implicit(DefaultColumnNames.Weight); var trainer = new FastTreeBinaryClassificationTrainer(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } else { return(trainer); } }, label, features, weights); return(rec.Output); }
/// <summary> /// FastTree <see cref="BinaryClassificationContext"/> extension method. /// Predict a target using a decision tree binary classificaiton model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</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 set of output columns including in order the predicted binary classification score (which will range /// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns> /// <example> /// <format type="text/markdown"> /// <![CDATA[ /// [!code-csharp[FastTree](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs)] /// ]]></format> /// </example> public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) FastTree(this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights, FastTreeBinaryClassificationTrainer.Options options, Action <IPredictorWithFeatureWeights <float> > onFit = null) { Contracts.CheckValueOrNull(options); CheckUserValues(label, features, weights, onFit); var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { var trainer = new FastTreeBinaryClassificationTrainer(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } else { return(trainer); } }, label, features, weights); return(rec.Output); }