/// <summary>
/// Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The binary classification catalog 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="enforceNonNegativity">Enforce non-negative weights.</param>
/// <param name="l1Regularization">Weight of L1 regularization term.</param>
/// <param name="l2Regularization">Weight of L2 regularization term.</param>
/// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer"/>. 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>
/// <returns>The predicted output.</returns>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) LbfgsLogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights = null,
float l1Regularization = Options.Defaults.L1Regularization,
float l2Regularization = Options.Defaults.L2Regularization,
float optimizationTolerance = Options.Defaults.OptimizationTolerance,
int historySize = Options.Defaults.HistorySize,
bool enforceNonNegativity = Options.Defaults.EnforceNonNegativity,
Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > onFit = null)
{
LbfgsStaticUtils.ValidateParams(label, features, weights, l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity, onFit);
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LbfgsLogisticRegressionBinaryTrainer(env, labelName, featuresName, weightsName,
l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity);
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.LbfgsLogisticRegressionBinaryTrainer"/> trainer.
/// </summary>
/// <param name="catalog">The binary classification catalog 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) LbfgsLogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
Scalar <bool> label,
Vector <float> features,
Scalar <float> weights,
Options options,
Action <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > 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.LabelColumnName = labelName;
options.FeatureColumnName = featuresName;
options.ExampleWeightColumnName = weightsName;
var trainer = new LbfgsLogisticRegressionBinaryTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
return(trainer);
}, label, features, weights);
return(rec.Output);
}
public static IEstimator <ITransformer> BuildTrainingPipeline(MLContext mlContext)
{
// Data process configuration with pipeline data transformations
var dataProcessPipeline = mlContext.Transforms.Categorical.OneHotEncoding(new[] { new InputOutputColumnPair("logged_in", "logged_in"), new InputOutputColumnPair("ns", "ns"), new InputOutputColumnPair("sample", "sample"), new InputOutputColumnPair("split", "split") })
.Append(mlContext.Transforms.Text.FeaturizeText("comment_tf", "comment"))
.Append(mlContext.Transforms.Concatenate("Features", new[] { "logged_in", "ns", "sample", "split", "comment_tf", "year" }))
.Append(mlContext.Transforms.NormalizeMinMax("Features", "Features"))
.AppendCacheCheckpoint(mlContext);
// Set the training algorithm
var trainer = mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(new LbfgsLogisticRegressionBinaryTrainer.Options()
{
L2Regularization = 0, 6806942f, L1Regularization = 0, 6276853f, OptimizationTolerance = 0, 0001f, HistorySize = 20, MaximumNumberOfIterations = 1294441415, InitialWeightsDiameter = 0, 8331606f, DenseOptimizer = true, LabelColumnName = "Label", FeatureColumnName = "Features"
});