/// <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">
/// <]
/// ]]></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">
/// <]
/// ]]></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">
/// <]
/// ]]></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">
/// <]
/// ]]></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">
/// <]
/// ]]></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">
/// <]
/// ]]></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);
}