/// <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); }
/// <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); }