/// <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); }
public MyAveragedPerceptron(IHostEnvironment env, AveragedPerceptronTrainer.Arguments args, string featureCol, string labelCol) : base(env, new TrainerInfo(caching: false), featureCol, labelCol) { _trainer = new AveragedPerceptronTrainer(env, args); }