/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param> /// <param name="labelColumn">The labelColumn column.</param> /// <param name="featureColumn">The features column.</param> /// <param name="advancedSettings">Algorithm advanced settings.</param> public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, Action <SymSgdClassificationTrainer.Arguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new SymSgdClassificationTrainer(env, labelColumn, featureColumn, advancedSettings)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the AveragedPerceptron trainer. /// </summary> /// <param name="ctx">The binary classification context trainer object.</param> /// <param name="options">Advanced arguments to the algorithm.</param> public static AveragedPerceptronTrainer AveragedPerceptron( this BinaryClassificationContext.BinaryClassificationTrainers ctx, AveragedPerceptronTrainer.Options options) { Contracts.CheckValue(ctx, nameof(ctx)); Contracts.CheckValue(options, nameof(options)); var env = CatalogUtils.GetEnvironment(ctx); return(new AveragedPerceptronTrainer(env, options)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param> /// <param name="options">Algorithm advanced options. See <see cref="SymSgdClassificationTrainer.Options"/>.</param> public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent( this BinaryClassificationContext.BinaryClassificationTrainers ctx, SymSgdClassificationTrainer.Options options) { Contracts.CheckValue(ctx, nameof(ctx)); Contracts.CheckValue(options, nameof(options)); var env = CatalogUtils.GetEnvironment(ctx); return(new SymSgdClassificationTrainer(env, options)); }
/// <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)); Contracts.CheckValue(options, nameof(options)); var env = CatalogUtils.GetEnvironment(ctx); return(new FastTreeBinaryClassificationTrainer(env, options)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="LinearSvm"/> trainer. /// </summary> /// <remarks> /// <para> /// The idea behind support vector machines, is to map instances into a high dimensional space /// in which the two classes are linearly separable, i.e., there exists a hyperplane such that all the positive examples are on one side of it, /// and all the negative examples are on the other. /// </para> /// <para> /// After this mapping, quadratic programming is used to find the separating hyperplane that maximizes the /// margin, i.e., the minimal distance between it and the instances. /// </para> /// </remarks> /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param> /// <param name="labelColumn">The name of the label column. </param> /// <param name="featureColumn">The name of the feature column.</param> /// <param name="weightsColumn">The optional name of the weights column.</param> /// <param name="numIterations">The number of training iteraitons.</param> /// <param name="advancedSettings">A delegate to supply more advanced arguments to the algorithm.</param> public static LinearSvm LinearSupportVectorMachines(this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weightsColumn = null, int numIterations = OnlineLinearArguments.OnlineDefaultArgs.NumIterations, Action <LinearSvm.Arguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); return(new LinearSvm(CatalogUtils.GetEnvironment(ctx), labelColumn, featureColumn, weightsColumn, numIterations, advancedSettings)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the SDCA trainer. /// </summary> /// <param name="ctx">The binary classification context trainer object.</param> /// <param name="options">Advanced arguments to the algorithm.</param> public static SdcaBinaryTrainer StochasticDualCoordinateAscent( this BinaryClassificationContext.BinaryClassificationTrainers ctx, SdcaBinaryTrainer.Options options) { Contracts.CheckValue(ctx, nameof(ctx)); Contracts.CheckValue(options, nameof(options)); var env = CatalogUtils.GetEnvironment(ctx); return(new SdcaBinaryTrainer(env, options)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the SDCA trainer, and a custom loss. /// Note that because we cannot be sure that all loss functions will produce naturally calibrated outputs, setting /// a custom loss function will not produce a calibrated probability column. /// </summary> /// <param name="ctx">The binary classification context trainer object.</param> /// <param name="label">The label, or dependent variable.</param> /// <param name="features">The features, or independent variables.</param> /// <param name="loss">The custom loss.</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="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, as well as the calibrator on top of that model. 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), and the predicted label.</returns> /// <seealso cref="Sdca(BinaryClassificationContext.BinaryClassificationTrainers, Scalar{bool}, Vector{float}, Scalar{float}, float?, float?, int?, Action{LinearBinaryPredictor, ParameterMixingCalibratedPredictor})"/> public static (Scalar <float> score, Scalar <bool> predictedLabel) Sdca( this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, ISupportSdcaClassificationLoss loss, Scalar <float> weights = null, float?l2Const = null, float?l1Threshold = null, int?maxIterations = null, Action <LinearBinaryPredictor> onFit = null ) { Contracts.CheckValue(label, nameof(label)); Contracts.CheckValue(features, nameof(features)); Contracts.CheckValue(loss, nameof(loss)); 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(onFit); bool hasProbs = loss is LogLoss; var args = new LinearClassificationTrainer.Arguments() { L2Const = l2Const, L1Threshold = l1Threshold, MaxIterations = maxIterations, LossFunction = new TrivialSdcaClassificationLossFactory(loss) }; var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration( (env, labelName, featuresName, weightsName) => { var trainer = new LinearClassificationTrainer(env, args, featuresName, labelName, weightsName); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => { var model = trans.Model; if (model is ParameterMixingCalibratedPredictor cali) { onFit((LinearBinaryPredictor)cali.SubPredictor); } else { onFit((LinearBinaryPredictor)model); } })); } return(trainer); }, label, features, weights, hasProbs); return(rec.Output); }
/// <summary> /// Predict a target using a decision tree regression model trained with the <see cref="FastTreeRegressionTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param> /// <param name="labelColumn">The labelColumn column.</param> /// <param name="featureColumn">The featureColumn column.</param> /// <param name="weights">The optional weights column.</param> /// <param name="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param> /// <param name="learningRate">The learning rate.</param> /// <param name="advancedSettings">Algorithm advanced settings.</param> public static BinaryClassificationGamTrainer GeneralizedAdditiveModels(this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weights = null, int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves, double learningRate = Defaults.LearningRates, Action <BinaryClassificationGamTrainer.Arguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new BinaryClassificationGamTrainer(env, labelColumn, featureColumn, weights, minDatapointsInLeaves, learningRate, advancedSettings)); }
/// <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="label">The label column.</param> /// <param name="features">The features column.</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 the tree, out of the subsampled data.</param> /// <param name="learningRate">The learning rate.</param> /// <param name="advancedSettings">Algorithm advanced settings.</param> public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassificationContext.BinaryClassificationTrainers ctx, string label = DefaultColumnNames.Label, string features = DefaultColumnNames.Features, string weights = null, int numLeaves = Defaults.NumLeaves, int numTrees = Defaults.NumTrees, int minDatapointsInLeafs = Defaults.MinDocumentsInLeafs, double learningRate = Defaults.LearningRates, Action<FastTreeBinaryClassificationTrainer.Arguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return new FastTreeBinaryClassificationTrainer(env, label, features, weights, numLeaves, numTrees, minDatapointsInLeafs, learningRate, advancedSettings); }
/// <summary> /// Predict a target using a decision tree regression model trained with the <see cref="FastForestClassification"/>. /// </summary> /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param> /// <param name="labelColumn">The labelColumn column.</param> /// <param name="featureColumn">The featureColumn column.</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="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param> /// <param name="learningRate">The learning rate.</param> public static FastForestClassification FastForest(this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weights = null, int numLeaves = Defaults.NumLeaves, int numTrees = Defaults.NumTrees, int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves, double learningRate = Defaults.LearningRates) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new FastForestClassification(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the AveragedPerceptron trainer, and a custom loss. /// </summary> /// <param name="ctx">The binary classification context trainer object.</param> /// <param name="label">The label, or dependent variable.</param> /// <param name="features">The features, or independent variables.</param> /// <param name="lossFunction">The custom loss.</param> /// <param name="weights">The optional example weights.</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="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, as well as the calibrator on top of that model. 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), and the predicted label.</returns> /// <seealso cref="AveragedPerceptronTrainer"/>. public static (Scalar <float> score, Scalar <bool> predictedLabel) AveragedPerceptron( this BinaryClassificationContext.BinaryClassificationTrainers ctx, IClassificationLoss lossFunction, Scalar <bool> label, Vector <float> features, Scalar <float> weights = null, float learningRate = AveragedLinearArguments.AveragedDefaultArgs.LearningRate, bool decreaseLearningRate = AveragedLinearArguments.AveragedDefaultArgs.DecreaseLearningRate, float l2RegularizerWeight = AveragedLinearArguments.AveragedDefaultArgs.L2RegularizerWeight, int numIterations = AveragedLinearArguments.AveragedDefaultArgs.NumIterations, Action <LinearBinaryPredictor> onFit = null ) { OnlineLinearStaticUtils.CheckUserParams(label, features, weights, learningRate, l2RegularizerWeight, numIterations, onFit); bool hasProbs = lossFunction is HingeLoss; var args = new AveragedPerceptronTrainer.Arguments() { LearningRate = learningRate, DecreaseLearningRate = decreaseLearningRate, L2RegularizerWeight = l2RegularizerWeight, NumIterations = numIterations }; if (lossFunction != null) { args.LossFunction = new TrivialClassificationLossFactory(lossFunction); } var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration( (env, labelName, featuresName, weightsName) => { args.FeatureColumn = featuresName; args.LabelColumn = labelName; args.InitialWeights = weightsName; var trainer = new AveragedPerceptronTrainer(env, args); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } else { return(trainer); } }, label, features, weights, hasProbs); return(rec.Output); }
/// <summary> /// Predict a target using a decision tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param> /// <param name="labelColumn">The labelColumn column.</param> /// <param name="featureColumn">The features column.</param> /// <param name="weights">The weights column.</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 static LightGbmBinaryTrainer LightGbm(this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weights = null, int?numLeaves = null, int?minDataPerLeaf = null, double?learningRate = null, int numBoostRound = LightGbmArguments.Defaults.NumBoostRound, Action <LightGbmArguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new LightGbmBinaryTrainer(env, labelColumn, featureColumn, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the SDCA trainer. /// </summary> /// <param name="ctx">The binary classification context trainer object.</param> /// <param name="labelColumn">The labelColumn, 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. Defaults to log-loss if not specified.</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> /// <example> /// <format type="text/markdown"> /// <![CDATA[ /// [!code-csharp[SDCA](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/SDCA.cs)] /// ]]></format> /// </example> public static SdcaBinaryTrainer StochasticDualCoordinateAscent( this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weights = null, ISupportSdcaClassificationLoss loss = null, float?l2Const = null, float?l1Threshold = null, int?maxIterations = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new SdcaBinaryTrainer(env, labelColumn, featureColumn, weights, loss, l2Const, l1Threshold, maxIterations)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Learners.LogisticRegression"/> trainer. /// </summary> /// <param name="ctx">The binary classificaiton context trainer object.</param> /// <param name="labelColumn">The label column name, or dependent variable.</param> /// <param name="featureColumn">The features, or independent variables.</param> /// <param name="weights">The optional example weights.</param> /// <param name="enforceNoNegativity">Enforce non-negative weights.</param> /// <param name="l1Weight">Weight of L1 regularization term.</param> /// <param name="l2Weight">Weight of L2 regularization term.</param> /// <param name="memorySize">Memory size for <see cref="Microsoft.ML.Learners.LogisticRegression"/>. Low=faster, less accurate.</param> /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param> public static LogisticRegression LogisticRegression(this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weights = null, float l1Weight = LROptions.Defaults.L1Weight, float l2Weight = LROptions.Defaults.L2Weight, float optimizationTolerance = LROptions.Defaults.OptTol, int memorySize = LROptions.Defaults.MemorySize, bool enforceNoNegativity = LROptions.Defaults.EnforceNonNegativity) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new LogisticRegression(env, labelColumn, featureColumn, weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enforceNoNegativity)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>. /// </summary> /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param> /// <param name="labelColumn">The label column.</param> /// <param name="featureColumn">The features column.</param> public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent( this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); var options = new SymSgdClassificationTrainer.Options { LabelColumn = labelColumn, FeatureColumn = featureColumn, }; return(new SymSgdClassificationTrainer(env, options)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the AveragedPerceptron trainer, and a custom loss. /// </summary> /// <param name="ctx">The binary classification context trainer object.</param> /// <param name="labelColumn">The name of the label column, or dependent variable.</param> /// <param name="featureColumn">The features, or independent variables.</param> /// <param name="lossFunction">The custom loss.</param> /// <param name="weights">The optional example weights.</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="advancedSettings">A delegate to supply more advanced arguments to the algorithm.</param> public static AveragedPerceptronTrainer AveragedPerceptron( this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weights = null, IClassificationLoss lossFunction = null, float learningRate = AveragedLinearArguments.AveragedDefaultArgs.LearningRate, bool decreaseLearningRate = AveragedLinearArguments.AveragedDefaultArgs.DecreaseLearningRate, float l2RegularizerWeight = AveragedLinearArguments.AveragedDefaultArgs.L2RegularizerWeight, int numIterations = AveragedLinearArguments.AveragedDefaultArgs.NumIterations, Action <AveragedPerceptronTrainer.Arguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new AveragedPerceptronTrainer(env, labelColumn, featureColumn, weights, lossFunction ?? new LogLoss(), learningRate, decreaseLearningRate, l2RegularizerWeight, numIterations, advancedSettings)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the SDCA trainer, and a custom loss. /// Note that because we cannot be sure that all loss functions will produce naturally calibrated outputs, setting /// a custom loss function will not produce a calibrated probability column. /// </summary> /// <param name="ctx">The binary classification context trainer object.</param> /// <param name="label">The label, or dependent variable.</param> /// <param name="features">The features, or independent variables.</param> /// <param name="loss">The custom loss.</param> /// <param name="weights">The optional example weights.</param> /// <param name="options">Advanced arguments to the algorithm.</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, as well as the calibrator on top of that model. 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), and the predicted label.</returns> public static (Scalar <float> score, Scalar <bool> predictedLabel) Sdca( this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights, ISupportSdcaClassificationLoss loss, SdcaBinaryTrainer.Options options, Action <LinearBinaryModelParameters> onFit = null ) { Contracts.CheckValue(label, nameof(label)); Contracts.CheckValue(features, nameof(features)); Contracts.CheckValueOrNull(weights); Contracts.CheckValueOrNull(options); Contracts.CheckValueOrNull(onFit); bool hasProbs = loss is LogLoss; var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration( (env, labelName, featuresName, weightsName) => { options.FeatureColumn = featuresName; options.LabelColumn = labelName; var trainer = new SdcaBinaryTrainer(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => { var model = trans.Model; if (model is ParameterMixingCalibratedPredictor cali) { onFit((LinearBinaryModelParameters)cali.SubPredictor); } else { onFit((LinearBinaryModelParameters)model); } })); } return(trainer); }, label, features, weights, hasProbs); return(rec.Output); }
/// <summary> /// Predict a target using a linear binary classification model trained with the SDCA trainer, and log-loss. /// </summary> /// <param name="ctx">The binary classification 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="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, as well as the calibrator on top of that model. 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> public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) Sdca( this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights = null, float?l2Const = null, float?l1Threshold = null, int?maxIterations = null, Action <LinearBinaryPredictor, ParameterMixingCalibratedPredictor> 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(onFit); var args = new LinearClassificationTrainer.Arguments() { L2Const = l2Const, L1Threshold = l1Threshold, MaxIterations = maxIterations, }; var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { var trainer = new LinearClassificationTrainer(env, args, featuresName, labelName, weightsName); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => { // Under the default log-loss we assume a calibrated predictor. var model = trans.Model; var cali = (ParameterMixingCalibratedPredictor)model; var pred = (LinearBinaryPredictor)cali.SubPredictor; onFit(pred, cali); })); } return(trainer); }, label, features, weights); return(rec.Output); }
/// <summary> /// Predict a target using a linear binary classification model trained with the AveragedPerceptron trainer, and a custom loss. /// </summary> /// <param name="ctx">The binary classification context trainer object.</param> /// <param name="label">The label, or dependent variable.</param> /// <param name="features">The features, or independent variables.</param> /// <param name="lossFunction">The custom loss.</param> /// <param name="weights">The optional example weights.</param> /// <param name="options">Advanced arguments to the algorithm.</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, as well as the calibrator on top of that model. 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), and the predicted label.</returns> /// <seealso cref="AveragedPerceptronTrainer"/>. /// <example> /// <format type="text/markdown"> /// <![CDATA[ /// [!code-csharp[AveragedPerceptron](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/AveragedPerceptronBinaryClassification.cs)] /// ]]></format> /// </example> public static (Scalar <float> score, Scalar <bool> predictedLabel) AveragedPerceptron( this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights, IClassificationLoss lossFunction, AveragedPerceptronTrainer.Options options, Action <LinearBinaryModelParameters> onFit = null ) { Contracts.CheckValue(label, nameof(label)); Contracts.CheckValue(features, nameof(features)); Contracts.CheckValueOrNull(weights); Contracts.CheckValueOrNull(options); Contracts.CheckValueOrNull(onFit); bool hasProbs = lossFunction is LogLoss; var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration( (env, labelName, featuresName, weightsName) => { options.LabelColumn = labelName; options.FeatureColumn = featuresName; options.InitialWeights = weightsName; var trainer = new AveragedPerceptronTrainer(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } else { return(trainer); } }, label, features, weights, hasProbs); return(rec.Output); }
/// <summary> /// Predict a target using a linear binary classification model trained with the AveragedPerceptron trainer, and a custom loss. /// </summary> /// <param name="ctx">The binary classification context trainer object.</param> /// <param name="label">The label, or dependent variable.</param> /// <param name="features">The features, or independent variables.</param> /// <param name="lossFunction">The custom loss.</param> /// <param name="weights">The optional example weights.</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="advancedSettings">A delegate to supply more avdanced arguments to the algorithm.</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, as well as the calibrator on top of that model. 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), and the predicted label.</returns> /// <seealso cref="AveragedPerceptronTrainer"/>. /// <example> /// <format type="text/markdown"> /// <![CDATA[ /// [!code-csharp[AveragedPerceptron](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/AveragedPerceptronBinaryClassification.cs)] /// ]]></format> /// </example> public static (Scalar <float> score, Scalar <bool> predictedLabel) AveragedPerceptron( this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights = null, IClassificationLoss lossFunction = null, float learningRate = AveragedLinearArguments.AveragedDefaultArgs.LearningRate, bool decreaseLearningRate = AveragedLinearArguments.AveragedDefaultArgs.DecreaseLearningRate, float l2RegularizerWeight = AveragedLinearArguments.AveragedDefaultArgs.L2RegularizerWeight, int numIterations = AveragedLinearArguments.AveragedDefaultArgs.NumIterations, Action <AveragedPerceptronTrainer.Arguments> advancedSettings = null, Action <LinearBinaryModelParameters> onFit = null ) { OnlineLinearStaticUtils.CheckUserParams(label, features, weights, learningRate, l2RegularizerWeight, numIterations, onFit, advancedSettings); bool hasProbs = lossFunction is LogLoss; var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration( (env, labelName, featuresName, weightsName) => { var trainer = new AveragedPerceptronTrainer(env, labelName, featuresName, weightsName, lossFunction, learningRate, decreaseLearningRate, l2RegularizerWeight, numIterations, advancedSettings); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } else { return(trainer); } }, label, features, weights, hasProbs); return(rec.Output); }
/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="StochasticGradientDescentClassificationTrainer"/> trainer. /// </summary> /// <param name="ctx">The binary classificaiton context trainer object.</param> /// <param name="labelColumn">The name of the label column.</param> /// <param name="featureColumn">The name of the feature column.</param> /// <param name="weights">The name for the example weight column.</param> /// <param name="maxIterations">The maximum number of iterations; set to 1 to simulate online learning.</param> /// <param name="initLearningRate">The initial learning rate used by SGD.</param> /// <param name="l2Weight">The L2 regularization constant.</param> /// <param name="loss">The loss function to use.</param> /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param> public static StochasticGradientDescentClassificationTrainer StochasticGradientDescent(this BinaryClassificationContext.BinaryClassificationTrainers ctx, string labelColumn = DefaultColumnNames.Label, string featureColumn = DefaultColumnNames.Features, string weights = null, int maxIterations = SgdArguments.Defaults.MaxIterations, double initLearningRate = SgdArguments.Defaults.InitLearningRate, float l2Weight = SgdArguments.Defaults.L2Weight, ISupportClassificationLossFactory loss = null, Action <SgdArguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new StochasticGradientDescentClassificationTrainer(env, labelColumn, featureColumn, weights, maxIterations, initLearningRate, l2Weight, loss, advancedSettings)); }
/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Runtime.Learners.LogisticRegression"/> trainer. /// </summary> /// <param name="ctx">The binary classificaiton 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="enoforceNoNegativity">Enforce non-negative weights.</param> /// <param name="l1Weight">Weight of L1 regularization term.</param> /// <param name="l2Weight">Weight of L2 regularization term.</param> /// <param name="memorySize">Memory size for <see cref="Microsoft.ML.Runtime.Learners.LogisticRegression"/>. Low=faster, less accurate.</param> /// <param name="optimizationTolerance">Threshold for optimizer convergence.</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> /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param> /// <returns>The predicted output.</returns> public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LogisticRegressionBinaryClassifier(this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights = null, float l1Weight = Arguments.Defaults.L1Weight, float l2Weight = Arguments.Defaults.L2Weight, float optimizationTolerance = Arguments.Defaults.OptTol, int memorySize = Arguments.Defaults.MemorySize, bool enoforceNoNegativity = Arguments.Defaults.EnforceNonNegativity, Action <Arguments> advancedSettings = null, Action <ParameterMixingCalibratedPredictor> onFit = null) { LbfgsStaticUtils.ValidateParams(label, features, weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enoforceNoNegativity, advancedSettings, onFit); var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { var trainer = new LogisticRegression(env, labelName, featuresName, weightsName, l1Weight, l2Weight, optimizationTolerance, memorySize, enoforceNoNegativity, advancedSettings); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } 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); }
/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Learners.LogisticRegression"/> trainer. /// </summary> /// <param name="ctx">The binary classificaiton 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="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> /// <param name="options">Advanced arguments to the algorithm.</param> /// <returns>The predicted output.</returns> public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LogisticRegressionBinaryClassifier(this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights, Options options, Action <ParameterMixingCalibratedPredictor> onFit = null) { Contracts.CheckValue(label, nameof(label)); Contracts.CheckValue(features, nameof(features)); Contracts.CheckValue(options, nameof(options)); Contracts.CheckValueOrNull(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 LogisticRegression(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, weights); return(rec.Output); }
/// <summary> /// Predict a target using a tree binary classification model trained with the <see cref="LightGbmBinaryTrainer"/>. /// </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 weights column.</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">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[LightGBM](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Static/LightGBMBinaryClassification.cs)] /// ]]></format> /// </example> public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LightGbm(this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights = null, int?numLeaves = null, int?minDataPerLeaf = null, double?learningRate = null, int numBoostRound = LightGbmArguments.Defaults.NumBoostRound, Action <LightGbmArguments> advancedSettings = null, Action <IPredictorWithFeatureWeights <float> > onFit = null) { CheckUserValues(label, features, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings, onFit); var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { var trainer = new LightGbmBinaryTrainer(env, labelName, featuresName, weightsName, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } else { return(trainer); } }, label, features, weights); return(rec.Output); }
/// <summary> /// Predict a target using a field-aware factorization machine. /// </summary> /// <param name="ctx">The binary classifier context trainer object.</param> /// <param name="label">The label, or dependent variable.</param> /// <param name="features">The features, or independent variables.</param> /// <param name="learningRate">Initial learning rate.</param> /// <param name="numIterations">Number of training iterations.</param> /// <param name="numLatentDimensions">Latent space dimensions.</param> /// <param name="advancedSettings">A delegate to set more 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 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> score, Scalar <bool> predictedLabel) FieldAwareFactorizationMachine(this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float>[] features, float learningRate = 0.1f, int numIterations = 5, int numLatentDimensions = 20, Action <FieldAwareFactorizationMachineTrainer.Arguments> advancedSettings = null, Action <FieldAwareFactorizationMachinePredictor> onFit = null) { Contracts.CheckValue(label, nameof(label)); Contracts.CheckNonEmpty(features, nameof(features)); Contracts.CheckParam(learningRate > 0, nameof(learningRate), "Must be positive"); Contracts.CheckParam(numIterations > 0, nameof(numIterations), "Must be positive"); Contracts.CheckParam(numLatentDimensions > 0, nameof(numLatentDimensions), "Must be positive"); Contracts.CheckValueOrNull(advancedSettings); Contracts.CheckValueOrNull(onFit); var rec = new CustomReconciler((env, labelCol, featureCols) => { var trainer = new FieldAwareFactorizationMachineTrainer(env, labelCol, featureCols, advancedSettings: args => { advancedSettings?.Invoke(args); args.LearningRate = learningRate; args.Iters = numIterations; args.LatentDim = numLatentDimensions; }); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } else { return(trainer); } }, label, features); return(rec.Output); }
/// <summary> /// Predict a target using a field-aware factorization machine algorithm. /// </summary> /// <param name="ctx">The binary classification 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="advancedSettings">A delegate to set more settings.</param> /// <returns></returns> public static FieldAwareFactorizationMachineTrainer FieldAwareFactorizationMachine(this BinaryClassificationContext.BinaryClassificationTrainers ctx, string label, string[] features, string weights = null, Action <FieldAwareFactorizationMachineTrainer.Arguments> advancedSettings = null) { Contracts.CheckValue(ctx, nameof(ctx)); var env = CatalogUtils.GetEnvironment(ctx); return(new FieldAwareFactorizationMachineTrainer(env, label, features, weights, advancedSettings: advancedSettings)); }
/// <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="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="minDatapointsInLeaves">The minimal number of datapoints allowed in a leaf of the 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{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 = null, int numLeaves = Defaults.NumLeaves, int numTrees = Defaults.NumTrees, int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves, double learningRate = Defaults.LearningRates, Action <FastTreeBinaryClassificationTrainer.Arguments> advancedSettings = null, Action <IPredictorWithFeatureWeights <float> > onFit = null) { CheckUserValues(label, features, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate, advancedSettings, onFit); var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { var trainer = new FastTreeBinaryClassificationTrainer(env, labelName, featuresName, weightsName, numLeaves, numTrees, minDatapointsInLeaves, learningRate, advancedSettings); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } else { return(trainer); } }, label, features, weights); return(rec.Output); }
/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer"/> trainer. /// </summary> /// <param name="ctx">The binary classificaiton context trainer object.</param> /// <param name="label">The name of the label column.</param> /// <param name="features">The name of the feature column.</param> /// <param name="weights">The name for the example weight column.</param> /// <param name="options">Advanced arguments to the algorithm.</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> score, Scalar <float> probability, Scalar <bool> predictedLabel) StochasticGradientDescentClassificationTrainer(this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights, Options options, Action <IPredictorWithFeatureWeights <float> > onFit = null) { var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { options.FeatureColumn = featuresName; options.LabelColumn = labelName; options.WeightColumn = weightsName != null ? Optional <string> .Explicit(weightsName) : Optional <string> .Implicit(DefaultColumnNames.Weight); var trainer = new StochasticGradientDescentClassificationTrainer(env, options); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, weights); return(rec.Output); }
/// <summary> /// Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer"/> trainer. /// </summary> /// <param name="ctx">The binary classificaiton context trainer object.</param> /// <param name="label">The name of the label column.</param> /// <param name="features">The name of the feature column.</param> /// <param name="weights">The name for the example weight column.</param> /// <param name="maxIterations">The maximum number of iterations; set to 1 to simulate online learning.</param> /// <param name="initLearningRate">The initial learning rate used by SGD.</param> /// <param name="l2Weight">The L2 regularization constant.</param> /// <param name="loss">The loss function to use.</param> /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</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> score, Scalar <float> probability, Scalar <bool> predictedLabel) StochasticGradientDescentClassificationTrainer(this BinaryClassificationContext.BinaryClassificationTrainers ctx, Scalar <bool> label, Vector <float> features, Scalar <float> weights = null, int maxIterations = Arguments.Defaults.MaxIterations, double initLearningRate = Arguments.Defaults.InitLearningRate, float l2Weight = Arguments.Defaults.L2Weight, ISupportClassificationLossFactory loss = null, Action <Arguments> advancedSettings = null, Action <IPredictorWithFeatureWeights <float> > onFit = null) { var rec = new TrainerEstimatorReconciler.BinaryClassifier( (env, labelName, featuresName, weightsName) => { var trainer = new StochasticGradientDescentClassificationTrainer(env, labelName, featuresName, weightsName, maxIterations, initLearningRate, l2Weight, loss, advancedSettings); if (onFit != null) { return(trainer.WithOnFitDelegate(trans => onFit(trans.Model))); } return(trainer); }, label, features, weights); return(rec.Output); }