/// <summary>
/// FastTree <see cref="RegressionContext"/> extension method.
/// </summary>
/// <param name="ctx">The <see cref="RegressionContext"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features colum.</param>
/// <param name="weights">The weights column.</param>
/// <param name="numLeaves">The number of leaves to use.</param>
/// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
/// <param name="minDocumentsInLeafs">The minimal number of documents allowed in a leaf of a regression 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{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 Score output column indicating the predicted value.</returns>
public static Scalar <float> FastTree(this RegressionContext.RegressionTrainers ctx,
Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
int numLeaves = Defaults.NumLeaves,
int numTrees = Defaults.NumTrees,
int minDocumentsInLeafs = Defaults.MinDocumentsInLeafs,
double learningRate = Defaults.LearningRates,
Action <FastTreeRegressionTrainer.Arguments> advancedSettings = null,
Action <FastTreeRegressionPredictor> onFit = null)
{
CheckUserValues(label, features, weights, numLeaves, numTrees, minDocumentsInLeafs, learningRate, advancedSettings, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new FastTreeRegressionTrainer(env, labelName, featuresName, weightsName, numLeaves,
numTrees, minDocumentsInLeafs, learningRate, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a linear regression model trained with the SDCA trainer.
/// </summary>
/// <param name="ctx">The regression 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="loss">The custom loss, if unspecified will be <see cref="SquaredLossSDCARegressionLossFunction"/>.</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{TInShape, TShape, 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 predicted output.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static Scalar <float> Sdca(this RegressionContext.RegressionTrainers ctx,
Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
ISupportSdcaRegressionLoss loss = null,
Action <SdcaRegressionTrainer.Arguments> advancedSettings = null,
Action <LinearRegressionPredictor> 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(loss);
Contracts.CheckValueOrNull(advancedSettings);
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SdcaRegressionTrainer(env, featuresName, labelName, weightsName, loss, l2Const, l1Threshold, maxIterations, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</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 Score output column indicating the predicted value.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static Scalar <float> LightGbm(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> 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 <LightGbmRegressionModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numLeaves, minDataPerLeaf, learningRate, numBoostRound, advancedSettings, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LightGbmRegressorTrainer(env, labelName, featuresName, weightsName, numLeaves,
minDataPerLeaf, learningRate, numBoostRound, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Runtime.Learners.LogisticRegression"/> trainer.
/// </summary>
/// <param name="ctx">The regression 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"/>. Lower=faster, less accurate.</param>
/// <param name="optimizationTolerance">Threshold for optimizer convergence.</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{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 predicted output.</returns>
public static Scalar <float> PoissonRegression(this RegressionContext.RegressionTrainers ctx,
Scalar <float> 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 <PoissonRegressionPredictor> onFit = null)
{
LbfgsStaticUtils.ValidateParams(label, features, weights, l1Weight, l2Weight, optimizationTolerance, memorySize, enoforceNoNegativity, advancedSettings, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new PoissonRegression(env, featuresName, labelName, weightsName,
l1Weight, l2Weight, optimizationTolerance, memorySize, enoforceNoNegativity);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Runtime.Learners.OnlineGradientDescentTrainer"/> trainer.
/// </summary>
/// <param name="ctx">The regression 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="lossFunction">The custom loss. Defaults to <see cref="SquaredLoss"/> if not provided.</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>
/// <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="OnlineGradientDescentTrainer"/>.
/// <returns>The predicted output.</returns>
public static Scalar <float> OnlineGradientDescent(this RegressionContext.RegressionTrainers ctx,
Scalar <float> label,
Vector <float> features,
Scalar <float> weights = null,
IRegressionLoss lossFunction = null,
float learningRate = OnlineGradientDescentTrainer.Arguments.OgdDefaultArgs.LearningRate,
bool decreaseLearningRate = OnlineGradientDescentTrainer.Arguments.OgdDefaultArgs.DecreaseLearningRate,
float l2RegularizerWeight = OnlineGradientDescentTrainer.Arguments.OgdDefaultArgs.L2RegularizerWeight,
int numIterations = OnlineLinearArguments.OnlineDefaultArgs.NumIterations,
Action <AveragedLinearArguments> advancedSettings = null,
Action <LinearRegressionPredictor> onFit = null)
{
OnlineLinearStaticUtils.CheckUserParams(label, features, weights, learningRate, l2RegularizerWeight, numIterations, onFit, advancedSettings);
Contracts.CheckValueOrNull(lossFunction);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new OnlineGradientDescentTrainer(env, labelName, featuresName, learningRate,
decreaseLearningRate, l2RegularizerWeight, numIterations, weightsName, lossFunction, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressionTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="weights">The 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 Score output column indicating the predicted value.</returns>
public static Scalar <float> LightGbm(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Vector <float> features, Scalar <float> weights,
LightGbmRegressionTrainer.Options options,
Action <LightGbmRegressionModelParameters> onFit = null)
{
Contracts.CheckValue(options, nameof(options));
CheckUserValues(label, features, weights, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
options.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
options.ExampleWeightColumnName = weightsName;
var trainer = new LightGbmRegressionTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a linear regression model trained with the SDCA trainer.
/// </summary>
/// <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="loss">The custom loss, if unspecified will be <see cref="SquaredLossSDCARegressionLossFunction"/>.</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> PredictSdcaRegression(this Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
ISupportSdcaRegressionLoss loss = null,
Action <LinearRegressionPredictor> 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");
Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative");
Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified");
Contracts.CheckValueOrNull(loss);
Contracts.CheckValueOrNull(onFit);
var args = new SdcaRegressionTrainer.Arguments()
{
L2Const = l2Const,
L1Threshold = l1Threshold,
MaxIterations = maxIterations
};
if (loss != null)
{
args.LossFunction = new TrivialRegressionLossFactory(loss);
}
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new SdcaRegressionTrainer(env, args, featuresName, labelName, weightsName);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressionTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="weights">The weights column.</param>
/// <param name="numberOfLeaves">The number of leaves to use.</param>
/// <param name="minimumExampleCountPerLeaf">The minimal number of data points allowed in a leaf of the tree, out of the subsampled data.</param>
/// <param name="learningRate">The learning rate.</param>
/// <param name="numberOfIterations">Number of iterations.</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 Score output column indicating the predicted value.</returns>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static Scalar <float> LightGbm(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Vector <float> features, Scalar <float> weights = null,
int?numberOfLeaves = null,
int?minimumExampleCountPerLeaf = null,
double?learningRate = null,
int numberOfIterations = Options.Defaults.NumberOfIterations,
Action <LightGbmRegressionModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, numberOfLeaves, minimumExampleCountPerLeaf, learningRate, numberOfIterations, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LightGbmRegressionTrainer(env, labelName, featuresName, weightsName, numberOfLeaves,
minimumExampleCountPerLeaf, learningRate, numberOfIterations);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}
/// <summary>
/// Predict a target using a tree regression model trained with the <see cref="LightGbmRegressorTrainer"/>.
/// </summary>
/// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
/// <param name="label">The label column.</param>
/// <param name="features">The features column.</param>
/// <param name="weights">The 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 Score output column indicating the predicted value.</returns>
public static Scalar <float> LightGbm(this RegressionCatalog.RegressionTrainers catalog,
Scalar <float> label, Vector <float> features, Scalar <float> weights,
Options options,
Action <LightGbmRegressionModelParameters> onFit = null)
{
CheckUserValues(label, features, weights, options, onFit);
var rec = new TrainerEstimatorReconciler.Regression(
(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 LightGbmRegressorTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Score);
}