public void TestEstimatorSymSgdInitPredictor() { (var pipe, var dataView) = GetBinaryClassificationPipeline(); var transformedData = pipe.Fit(dataView).Transform(dataView); var args = new LinearClassificationTrainer.Arguments(); var initPredictor = new LinearClassificationTrainer(Env, args, "Features", "Label").Fit(transformedData); var data = initPredictor.Transform(transformedData); var withInitPredictor = new SymSgdClassificationTrainer(Env, "Features", "Label").Train(transformedData, initialPredictor: initPredictor.Model); var outInitData = withInitPredictor.Transform(transformedData); var notInitPredictor = new SymSgdClassificationTrainer(Env, "Features", "Label").Train(transformedData); var outNoInitData = notInitPredictor.Transform(transformedData); int numExamples = 10; var col1 = data.GetColumn <float>(Env, "Score").Take(numExamples).ToArray(); var col2 = outInitData.GetColumn <float>(Env, "Score").Take(numExamples).ToArray(); var col3 = outNoInitData.GetColumn <float>(Env, "Score").Take(numExamples).ToArray(); bool col12Diff = default; bool col23Diff = default; bool col13Diff = default; for (int i = 0; i < numExamples; i++) { col12Diff = col12Diff || (col1[i] != col2[i]); col23Diff = col23Diff || (col2[i] != col3[i]); col13Diff = col13Diff || (col1[i] != col3[i]); } Contracts.Assert(col12Diff && col23Diff && col13Diff); Done(); }
/// <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 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); }
public MySdca(IHostEnvironment env, LinearClassificationTrainer.Arguments args, string featureCol, string labelCol) : base(env, new TrainerInfo(), featureCol, labelCol) { _args = args; }