Uses of Class
org.tribuo.regression.Regressor
Packages that use Regressor
Package
Description
Provides an implementation of LIME (Locally Interpretable Model Explanations).
Code for uploading models to Oracle Cloud Infrastructure Data Science, and also for scoring models deployed
in Oracle Cloud Infrastructure Data Science.
This package contains a Tribuo wrapper around the ONNX Runtime.
Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported
Tribuo output type.
Provides classes and infrastructure for regression problems with single or multiple output dimensions.
Provides simple baseline regression predictors.
Provides
EnsembleCombiner
implementations
for working with multi-output regression problems.Evaluation classes for single or multi-dimensional regression.
Provides some example regression data generators for testing implementations.
Provides an interface to liblinear for regression problems.
Provides an interface to LibSVM for regression problems.
Provides an implementation of decision trees for regression problems.
Provides internal implementation classes for the regression trees.
Provides an implementation of factorization machines for regression using Stochastic Gradient Descent.
Provides an implementation of linear regression using Stochastic Gradient Descent.
Provides implementations of sparse linear regression using various forms of regularisation penalty.
Provides an interface to XGBoost for regression problems.
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Uses of Regressor in org.tribuo.classification.explanations.lime
Classes in org.tribuo.classification.explanations.lime that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
LIMEBase merges the lime_base.py and lime_tabular.py implementations, and deals with simple matrices of numerical or categorical data.class
Uses the columnar data processing infrastructure to mix text and tabular data.class
AnExplanation
using LIME.class
Uses a TribuoTextFeatureExtractor
to explain the prediction for a given piece of text.Fields in org.tribuo.classification.explanations.lime with type parameters of type RegressorModifier and TypeFieldDescriptionprotected final SparseTrainer
<Regressor> LIMEBase.explanationTrainer
protected static final OutputFactory
<Regressor> LIMEBase.regressionFactory
Methods in org.tribuo.classification.explanations.lime that return RegressorModifier and TypeMethodDescriptionstatic Regressor
LIMEBase.transformOutput
(Prediction<Label> prediction) Transforms aPrediction
for a multiclass problem into aRegressor
output which represents the probability for each class.Methods in org.tribuo.classification.explanations.lime that return types with arguments of type RegressorModifier and TypeMethodDescriptionprotected com.oracle.labs.mlrg.olcut.util.Pair
<LIMEExplanation, List<Example<Regressor>>> LIMEBase.explainWithSamples
(Example<Label> example) protected com.oracle.labs.mlrg.olcut.util.Pair
<LIMEExplanation, List<Example<Regressor>>> LIMEColumnar.explainWithSamples
(Map<String, String> input) LIMEExplanation.getModel()
LIMEText.sampleData
(String inputText, List<Token> tokens) Samples a new dataset from the input text.protected SparseModel
<Regressor> Trains the explanation model using the supplied sampled data and the input example.Method parameters in org.tribuo.classification.explanations.lime with type arguments of type RegressorModifier and TypeMethodDescriptionprotected SparseModel
<Regressor> Trains the explanation model using the supplied sampled data and the input example.protected SparseModel
<Regressor> Trains the explanation model using the supplied sampled data and the input example.Constructor parameters in org.tribuo.classification.explanations.lime with type arguments of type RegressorModifierConstructorDescriptionLIMEBase
(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples) Constructs a LIME explainer for a model which uses tabular data (i.e., no special treatment for text features).LIMEColumnar
(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples, RowProcessor<Label> exampleGenerator, Tokenizer tokenizer) Constructs a LIME explainer for a model which uses the columnar data processing system.LIMEExplanation
(SparseModel<Regressor> model, Prediction<Label> prediction, RegressionEvaluation evaluation) Constructs a LIME explanation.LIMEText
(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples, TextFeatureExtractor<Label> extractor, Tokenizer tokenizer) Constructs a LIME explainer for a model which uses text data. -
Uses of Regressor in org.tribuo.interop.oci
Classes in org.tribuo.interop.oci that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionfinal class
Methods in org.tribuo.interop.oci that return types with arguments of type RegressorModifier and TypeMethodDescriptionOCIRegressorConverter.convertOutput
(DenseMatrix scores, int[] numValidFeatures, List<Example<Regressor>> examples, ImmutableOutputInfo<Regressor> outputIDInfo) OCIRegressorConverter.convertOutput
(DenseVector scores, int numValidFeature, Example<Regressor> example, ImmutableOutputInfo<Regressor> outputIDInfo) Method parameters in org.tribuo.interop.oci with type arguments of type RegressorModifier and TypeMethodDescriptionOCIRegressorConverter.convertOutput
(DenseMatrix scores, int[] numValidFeatures, List<Example<Regressor>> examples, ImmutableOutputInfo<Regressor> outputIDInfo) OCIRegressorConverter.convertOutput
(DenseMatrix scores, int[] numValidFeatures, List<Example<Regressor>> examples, ImmutableOutputInfo<Regressor> outputIDInfo) OCIRegressorConverter.convertOutput
(DenseVector scores, int numValidFeature, Example<Regressor> example, ImmutableOutputInfo<Regressor> outputIDInfo) OCIRegressorConverter.convertOutput
(DenseVector scores, int numValidFeature, Example<Regressor> example, ImmutableOutputInfo<Regressor> outputIDInfo) -
Uses of Regressor in org.tribuo.interop.onnx
Classes in org.tribuo.interop.onnx that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
Methods in org.tribuo.interop.onnx that return RegressorModifier and TypeMethodDescriptionRegressorTransformer.transformToOutput
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo) Methods in org.tribuo.interop.onnx that return types with arguments of type RegressorModifier and TypeMethodDescriptionRegressorTransformer.transformToBatchOutput
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo) RegressorTransformer.transformToBatchPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples) RegressorTransformer.transformToPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example) Method parameters in org.tribuo.interop.onnx with type arguments of type RegressorModifier and TypeMethodDescriptionRegressorTransformer.transformToBatchOutput
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo) RegressorTransformer.transformToBatchPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples) RegressorTransformer.transformToOutput
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo) RegressorTransformer.transformToPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example) RegressorTransformer.transformToPrediction
(List<ai.onnxruntime.OnnxValue> tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example) -
Uses of Regressor in org.tribuo.interop.tensorflow
Classes in org.tribuo.interop.tensorflow that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
Methods in org.tribuo.interop.tensorflow that return RegressorModifier and TypeMethodDescriptionRegressorConverter.convertToOutput
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo) Methods in org.tribuo.interop.tensorflow that return types with arguments of type RegressorModifier and TypeMethodDescriptionRegressorConverter.convertToBatchOutput
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo) RegressorConverter.convertToBatchPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples) RegressorConverter.convertToPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example) Methods in org.tribuo.interop.tensorflow with parameters of type RegressorModifier and TypeMethodDescriptionorg.tensorflow.Tensor
RegressorConverter.convertToTensor
(Regressor example, ImmutableOutputInfo<Regressor> outputIDInfo) Method parameters in org.tribuo.interop.tensorflow with type arguments of type RegressorModifier and TypeMethodDescriptionRegressorConverter.convertToBatchOutput
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo) RegressorConverter.convertToBatchPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples) RegressorConverter.convertToBatchPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int[] numValidFeatures, List<Example<Regressor>> examples) RegressorConverter.convertToOutput
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo) RegressorConverter.convertToPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example) RegressorConverter.convertToPrediction
(org.tensorflow.Tensor tensor, ImmutableOutputInfo<Regressor> outputIDInfo, int numValidFeatures, Example<Regressor> example) org.tensorflow.Tensor
RegressorConverter.convertToTensor
(List<Example<Regressor>> examples, ImmutableOutputInfo<Regressor> outputIDInfo) org.tensorflow.Tensor
RegressorConverter.convertToTensor
(List<Example<Regressor>> examples, ImmutableOutputInfo<Regressor> outputIDInfo) org.tensorflow.Tensor
RegressorConverter.convertToTensor
(Regressor example, ImmutableOutputInfo<Regressor> outputIDInfo) -
Uses of Regressor in org.tribuo.regression
Subclasses of Regressor in org.tribuo.regressionModifier and TypeClassDescriptionstatic final class
ARegressor
which contains a single dimension, used internally when the model implementation doesn't natively support multi-dimensional regression outputs.Classes in org.tribuo.regression that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
AImmutableOutputInfo
forRegressor
s.class
AMutableOutputInfo
forRegressor
s.final class
A factory for creatingRegressor
s andRegressionInfo
s.class
The base class for regression information usingRegressor
s.class
AnOutput
for n-dimensional real valued regression.Fields in org.tribuo.regression declared as RegressorModifier and TypeFieldDescriptionstatic final Regressor
RegressionFactory.UNKNOWN_MULTIPLE_REGRESSOR
Deprecated.Deprecated when regression was made multidimensional by default.static final Regressor
RegressionFactory.UNKNOWN_REGRESSOR
The sentinel unknown regressor, used when there is no ground truth regressor value.Methods in org.tribuo.regression that return RegressorModifier and TypeMethodDescriptionRegressor.copy()
static Regressor
Regressor.createFromPairList
(List<com.oracle.labs.mlrg.olcut.util.Pair<String, Double>> dimensions) Creates a Regressor from a list of dimension tuples.<V> Regressor
RegressionFactory.generateOutput
(V label) Parses the Regressor value either by toStringing the input and callingparseString(java.lang.String)
or if it's a collection iterating over the elements calling toString on each element in turn and usingparseElement(int, java.lang.String)
.ImmutableRegressionInfo.getOutput
(int id) RegressionFactory.getUnknownOutput()
static Regressor
Regressor.parseString
(String s) Parses a string of the form:static Regressor
Regressor.parseString
(String s, char splitChar) Parses a string of the form:Methods in org.tribuo.regression that return types with arguments of type RegressorModifier and TypeMethodDescriptionRegressionFactory.constructInfoForExternalModel
(Map<Regressor, Integer> mapping) RegressionInfo.generateImmutableOutputInfo()
RegressionFactory.generateInfo()
RegressionInfo.generateMutableOutputInfo()
ImmutableRegressionInfo.getDomain()
RegressionInfo.getDomain()
Returns a set containing a Regressor for each dimension with the minimum value observed.RegressionFactory.getEvaluator()
ImmutableRegressionInfo.iterator()
Methods in org.tribuo.regression with parameters of type RegressorModifier and TypeMethodDescriptionboolean
Regressor.DimensionTuple.fullEquals
(Regressor other) boolean
Regressor.fullEquals
(Regressor other) int
void
Method parameters in org.tribuo.regression with type arguments of type RegressorModifier and TypeMethodDescriptionRegressionFactory.constructInfoForExternalModel
(Map<Regressor, Integer> mapping) boolean
ImmutableRegressionInfo.domainAndIDEquals
(ImmutableOutputInfo<Regressor> other) static String[]
Regressor.extractNames
(OutputInfo<Regressor> info) Extracts the names from the supplied Regressor domain in their canonical order. -
Uses of Regressor in org.tribuo.regression.baseline
Subclasses with type arguments of type Regressor in org.tribuo.regression.baselineModifier and TypeClassDescriptionclass
A model which performs dummy regressions (e.g., constant output, gaussian sampled output, mean value, median, quartile).Classes in org.tribuo.regression.baseline that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionfinal class
A trainer for simple baseline regressors.Methods in org.tribuo.regression.baseline that return types with arguments of type RegressorModifier and TypeMethodDescriptionDummyRegressionModel.copy
(String newName, ModelProvenance newProvenance) Method parameters in org.tribuo.regression.baseline with type arguments of type RegressorModifier and TypeMethodDescriptionDummyRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance) DummyRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount) -
Uses of Regressor in org.tribuo.regression.ensemble
Classes in org.tribuo.regression.ensemble that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
A combiner which performs a weighted or unweighted average of the predicted regressors independently across the output dimensions.Methods in org.tribuo.regression.ensemble that return types with arguments of type RegressorModifier and TypeMethodDescriptionAveragingCombiner.combine
(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions) AveragingCombiner.combine
(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions, float[] weights) Method parameters in org.tribuo.regression.ensemble with type arguments of type RegressorModifier and TypeMethodDescriptionAveragingCombiner.combine
(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions) AveragingCombiner.combine
(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions) AveragingCombiner.combine
(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions, float[] weights) AveragingCombiner.combine
(ImmutableOutputInfo<Regressor> outputInfo, List<Prediction<Regressor>> predictions, float[] weights) -
Uses of Regressor in org.tribuo.regression.evaluation
Subclasses with type arguments of type Regressor in org.tribuo.regression.evaluationModifier and TypeClassDescriptionfinal class
Subinterfaces with type arguments of type Regressor in org.tribuo.regression.evaluationModifier and TypeInterfaceDescriptioninterface
Defines methods that calculate regression performance.Classes in org.tribuo.regression.evaluation that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
AEvaluationMetric
forRegressor
s which calculates the metric based on a the true values and the predicted values.Methods in org.tribuo.regression.evaluation that return types with arguments of type RegressorModifier and TypeMethodDescriptionRegressionEvaluation.explainedVariance()
Calculatest the explained variance for all dimensions.RegressionMetric.getTarget()
RegressionEvaluation.mae()
Calculates the Mean Absolute Error for all dimensions.RegressionEvaluation.r2()
Calculates R2 for all dimensions.RegressionEvaluation.rmse()
Calculates the RMSE for all dimensions.Methods in org.tribuo.regression.evaluation with parameters of type RegressorModifier and TypeMethodDescriptiondouble
RegressionEvaluation.explainedVariance
(Regressor variable) Calculates the explained variance of the ground truth using the predictions for the supplied dimension.static double
RegressionMetrics.explainedVariance
(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates the explained variance based on the supplied statistics for a single dimension.double
Calculates the Mean Absolute Error for that dimension.static double
RegressionMetrics.mae
(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates the Mean Absolute Error based on the supplied statistics for a single dimension.double
Calculates R2 for the supplied dimension.static double
RegressionMetrics.r2
(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates R^2 based on the supplied statistics for a single dimension.double
Calculates the Root Mean Squared Error (i.e., the square root of the average squared errors across all data points) for the supplied dimension.static double
RegressionMetrics.rmse
(Regressor variable, RegressionSufficientStatistics sufficientStats) Calculates the RMSE based on the supplied statistics for a single dimension.Method parameters in org.tribuo.regression.evaluation with type arguments of type RegressorModifier and TypeMethodDescriptionprotected org.tribuo.regression.evaluation.RegressionMetric.Context
RegressionEvaluator.createContext
(Model<Regressor> model, List<Prediction<Regressor>> predictions) protected org.tribuo.regression.evaluation.RegressionMetric.Context
RegressionEvaluator.createContext
(Model<Regressor> model, List<Prediction<Regressor>> predictions) org.tribuo.regression.evaluation.RegressionMetric.Context
RegressionMetric.createContext
(Model<Regressor> model, List<Prediction<Regressor>> predictions) org.tribuo.regression.evaluation.RegressionMetric.Context
RegressionMetric.createContext
(Model<Regressor> model, List<Prediction<Regressor>> predictions) protected RegressionEvaluation
RegressionEvaluator.createEvaluation
(org.tribuo.regression.evaluation.RegressionMetric.Context context, Map<MetricID<Regressor>, Double> results, EvaluationProvenance provenance) protected Set
<RegressionMetric> RegressionEvaluator.createMetrics
(Model<Regressor> model) static double
RegressionMetrics.explainedVariance
(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates the explained variance based on the supplied statistics.static double
RegressionMetrics.mae
(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates the Mean Absolute Error based on the supplied statistics.static double
RegressionMetrics.r2
(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates R^2 based on the supplied statistics.static double
RegressionMetrics.rmse
(MetricTarget<Regressor> target, RegressionSufficientStatistics sufficientStats) Calculates the RMSE based on the supplied statistics.Constructor parameters in org.tribuo.regression.evaluation with type arguments of type RegressorModifierConstructorDescriptionRegressionMetric
(MetricTarget<Regressor> tgt, String name, ToDoubleBiFunction<MetricTarget<Regressor>, org.tribuo.regression.evaluation.RegressionMetric.Context> impl) Construct a newRegressionMetric
for the supplied metric target, using the supplied function.RegressionMetric
(MetricTarget<Regressor> tgt, String name, ToDoubleBiFunction<MetricTarget<Regressor>, org.tribuo.regression.evaluation.RegressionMetric.Context> impl) Construct a newRegressionMetric
for the supplied metric target, using the supplied function.RegressionMetric
(MetricTarget<Regressor> tgt, String name, ToDoubleBiFunction<MetricTarget<Regressor>, org.tribuo.regression.evaluation.RegressionMetric.Context> impl, boolean useExampleWeights) Construct a newRegressionMetric
for the supplied metric target, using the supplied function.RegressionMetric
(MetricTarget<Regressor> tgt, String name, ToDoubleBiFunction<MetricTarget<Regressor>, org.tribuo.regression.evaluation.RegressionMetric.Context> impl, boolean useExampleWeights) Construct a newRegressionMetric
for the supplied metric target, using the supplied function.RegressionSufficientStatistics
(ImmutableOutputInfo<Regressor> domain, List<Prediction<Regressor>> predictions, boolean useExampleWeights) Constructs the sufficient statistics for regression metrics.RegressionSufficientStatistics
(ImmutableOutputInfo<Regressor> domain, List<Prediction<Regressor>> predictions, boolean useExampleWeights) Constructs the sufficient statistics for regression metrics. -
Uses of Regressor in org.tribuo.regression.example
Classes in org.tribuo.regression.example that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
Generates a single dimensional output drawn from N(slope*x + intercept,variance).class
Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + intercept,variance).Methods in org.tribuo.regression.example that return types with arguments of type RegressorModifier and TypeMethodDescriptionRegressionDataGenerator.denseTrainTest()
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.RegressionDataGenerator.denseTrainTest()
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.RegressionDataGenerator.denseTrainTest
(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.RegressionDataGenerator.denseTrainTest
(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.RegressionDataGenerator.emptyExample()
Generates an example with no features.RegressionDataGenerator.emptyMultiDimExample()
Generates an example with no features.static MutableDataset
<Regressor> GaussianDataSource.generateDataset
(int numSamples, float slope, float intercept, float variance, float xMin, float xMax, long seed) Generates a single dimensional output drawn from N(slope*x + intercept,variance).static MutableDataset
<Regressor> NonlinearGaussianDataSource.generateDataset
(int numSamples, float[] weights, float intercept, float variance, float xZeroMin, float xZeroMax, float xOneMin, float xOneMax, long seed) Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + intercept,variance).GaussianDataSource.getOutputFactory()
NonlinearGaussianDataSource.getOutputFactory()
RegressionDataGenerator.invalidMultiDimSparseExample()
Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.RegressionDataGenerator.invalidSparseExample()
Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.GaussianDataSource.iterator()
NonlinearGaussianDataSource.iterator()
RegressionDataGenerator.multiDimDenseTrainTest()
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.RegressionDataGenerator.multiDimDenseTrainTest()
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.RegressionDataGenerator.multiDimDenseTrainTest
(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.RegressionDataGenerator.multiDimDenseTrainTest
(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.RegressionDataGenerator.multiDimSparseTrainTest()
Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.RegressionDataGenerator.multiDimSparseTrainTest()
Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.RegressionDataGenerator.multiDimSparseTrainTest
(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.RegressionDataGenerator.multiDimSparseTrainTest
(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.RegressionDataGenerator.sparseTrainTest()
Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.RegressionDataGenerator.sparseTrainTest()
Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.RegressionDataGenerator.sparseTrainTest
(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.RegressionDataGenerator.sparseTrainTest
(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.RegressionDataGenerator.threeDimDenseTrainTest
(double negate, boolean remapIndices) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.RegressionDataGenerator.threeDimDenseTrainTest
(double negate, boolean remapIndices) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}. -
Uses of Regressor in org.tribuo.regression.impl
Subclasses with type arguments of type Regressor in org.tribuo.regression.implModifier and TypeClassDescriptionclass
AModel
which wraps n independent regression models, where n is the size of the MultipleRegressor domain.class
ASparseModel
which wraps n independent regression models, where n is the size of the MultipleRegressor domain.Classes in org.tribuo.regression.impl that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
Base class for training n independent sparse models, one per dimension.class
Methods in org.tribuo.regression.impl that return types with arguments of type RegressorModifier and TypeMethodDescriptionMethod parameters in org.tribuo.regression.impl with type arguments of type RegressorModifier and TypeMethodDescriptionprotected SparseVector
SkeletalIndependentRegressionModel.createFeatures
(Example<Regressor> example) Creates the feature vector.protected SparseVector
SkeletalIndependentRegressionSparseModel.createFeatures
(Example<Regressor> example) Creates the feature vector.protected abstract SkeletalIndependentRegressionSparseModel
SkeletalIndependentRegressionSparseTrainer.createModel
(Map<String, T> models, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo) Constructs the appropriate subclass ofSkeletalIndependentRegressionModel
for this trainer.protected abstract SkeletalIndependentRegressionModel
SkeletalIndependentRegressionTrainer.createModel
(Map<String, T> models, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo) Constructs the appropriate subclass ofSkeletalIndependentRegressionModel
for this trainer.SkeletalIndependentRegressionSparseTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) SkeletalIndependentRegressionSparseTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) SkeletalIndependentRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) SkeletalIndependentRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) Constructor parameters in org.tribuo.regression.impl with type arguments of type RegressorModifierConstructorDescriptionprotected
SkeletalIndependentRegressionModel
(String name, String[] dimensions, ModelProvenance modelProvenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo) models.size() must equal labelInfo.getDomain().size()protected
SkeletalIndependentRegressionSparseModel
(String name, String[] dimensions, ModelProvenance modelProvenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo, Map<String, List<String>> activeFeatures) models.size() must equal labelInfo.getDomain().size() -
Uses of Regressor in org.tribuo.regression.liblinear
Subclasses with type arguments of type Regressor in org.tribuo.regression.liblinearModifier and TypeClassDescriptionclass
AModel
which wraps a LibLinear-java model.class
ATrainer
which wraps a liblinear-java regression trainer.Classes in org.tribuo.regression.liblinear that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionfinal class
The carrier type for liblinear linear regression modes.Methods in org.tribuo.regression.liblinear that return types with arguments of type RegressorModifier and TypeMethodDescriptionprotected LibLinearModel
<Regressor> LibLinearRegressionTrainer.createModel
(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models) LibLinearRegressionModel.innerGetExcuse
(Example<Regressor> e, double[][] allFeatureWeights) The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.Method parameters in org.tribuo.regression.liblinear with type arguments of type RegressorModifier and TypeMethodDescriptionprotected LibLinearModel
<Regressor> LibLinearRegressionTrainer.createModel
(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models) protected com.oracle.labs.mlrg.olcut.util.Pair
<de.bwaldvogel.liblinear.FeatureNode[][], double[][]> LibLinearRegressionTrainer.extractData
(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap) protected com.oracle.labs.mlrg.olcut.util.Pair
<de.bwaldvogel.liblinear.FeatureNode[][], double[][]> LibLinearRegressionTrainer.extractData
(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap) LibLinearRegressionModel.innerGetExcuse
(Example<Regressor> e, double[][] allFeatureWeights) The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses. -
Uses of Regressor in org.tribuo.regression.libsvm
Subclasses with type arguments of type Regressor in org.tribuo.regression.libsvmModifier and TypeClassDescriptionclass
A regression model that uses an underlying libSVM model to make the predictions.class
A trainer for regression models that uses LibSVM.Classes in org.tribuo.regression.libsvm that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
The carrier type for LibSVM regression modes.Methods in org.tribuo.regression.libsvm that return types with arguments of type RegressorModifier and TypeMethodDescriptionprotected LibSVMModel
<Regressor> LibSVMRegressionTrainer.createModel
(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputIDInfo, List<libsvm.svm_model> models) Method parameters in org.tribuo.regression.libsvm with type arguments of type RegressorModifier and TypeMethodDescriptionprotected LibSVMModel
<Regressor> LibSVMRegressionTrainer.createModel
(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputIDInfo, List<libsvm.svm_model> models) protected com.oracle.labs.mlrg.olcut.util.Pair
<libsvm.svm_node[][], double[][]> LibSVMRegressionTrainer.extractData
(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap) protected com.oracle.labs.mlrg.olcut.util.Pair
<libsvm.svm_node[][], double[][]> LibSVMRegressionTrainer.extractData
(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap) Constructor parameters in org.tribuo.regression.libsvm with type arguments of type RegressorModifierConstructorDescriptionLibSVMRegressionTrainer
(SVMParameters<Regressor> parameters) Constructs a LibSVMRegressionTrainer using the supplied parameters without standardizing the regression variables.LibSVMRegressionTrainer
(SVMParameters<Regressor> parameters, boolean standardize) Constructs a LibSVMRegressionTrainer using the supplied parameters andTrainer.DEFAULT_SEED
.LibSVMRegressionTrainer
(SVMParameters<Regressor> parameters, boolean standardize, long seed) Constructs a LibSVMRegressionTrainer using the supplied parameters and seed. -
Uses of Regressor in org.tribuo.regression.rtree
Subclasses with type arguments of type Regressor in org.tribuo.regression.rtreeModifier and TypeClassDescriptionclass
ATrainer
that uses an approximation of the CART algorithm to build a decision tree.final class
ATrainer
that uses an approximation of the CART algorithm to build a decision tree.final class
Methods in org.tribuo.regression.rtree that return types with arguments of type RegressorModifier and TypeMethodDescriptionIndependentRegressionTreeModel.getRoot()
Returns null, as this model contains multiple roots, one per regression output dimension.IndependentRegressionTreeModel.getRoots()
Returns an unmodifiable view on the root node collection.protected AbstractTrainingNode
<Regressor> CARTJointRegressionTrainer.mkTrainingNode
(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer) protected AbstractTrainingNode
<Regressor> CARTRegressionTrainer.mkTrainingNode
(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer) CARTRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) CARTRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) Method parameters in org.tribuo.regression.rtree with type arguments of type RegressorModifier and TypeMethodDescriptionprotected AbstractTrainingNode
<Regressor> CARTJointRegressionTrainer.mkTrainingNode
(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer) protected AbstractTrainingNode
<Regressor> CARTRegressionTrainer.mkTrainingNode
(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer) CARTRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) CARTRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) -
Uses of Regressor in org.tribuo.regression.rtree.impl
Subclasses with type arguments of type Regressor in org.tribuo.regression.rtree.implModifier and TypeClassDescriptionclass
A decision tree node used at training time.class
A decision tree node used at training time.Methods in org.tribuo.regression.rtree.impl that return types with arguments of type RegressorModifier and TypeMethodDescriptionJointRegressorTrainingNode.buildTree
(int[] featureIDs, SplittableRandom rng, boolean useRandomSplitPoints) Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).RegressorTrainingNode.buildTree
(int[] featureIDs, SplittableRandom rng, boolean useRandomSplitPoints) Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).JointRegressorTrainingNode.convertTree()
RegressorTrainingNode.convertTree()
Method parameters in org.tribuo.regression.rtree.impl with type arguments of type RegressorModifier and TypeMethodDescriptionRegressorTrainingNode.invertData
(Dataset<Regressor> examples) Inverts a training dataset from row major to column major.Constructor parameters in org.tribuo.regression.rtree.impl with type arguments of type RegressorModifierConstructorDescriptionJointRegressorTrainingNode
(RegressorImpurity impurity, Dataset<Regressor> examples, boolean normalize, AbstractTrainingNode.LeafDeterminer leafDeterminer) Constructor which creates the inverted file.RegressorTrainingNode
(RegressorImpurity impurity, RegressorTrainingNode.InvertedData tuple, int dimIndex, String dimName, int numExamples, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Regressor> outputInfo, AbstractTrainingNode.LeafDeterminer leafDeterminer) Constructs a tree training node for regression problems. -
Uses of Regressor in org.tribuo.regression.sgd.fm
Subclasses with type arguments of type Regressor in org.tribuo.regression.sgd.fmModifier and TypeClassDescriptionclass
The inference time model of a regression factorization machine trained using SGD.class
A trainer for a regression factorization machine using SGD.Methods in org.tribuo.regression.sgd.fm that return types with arguments of type RegressorModifier and TypeMethodDescriptionMethods in org.tribuo.regression.sgd.fm with parameters of type RegressorModifier and TypeMethodDescriptionprotected DenseVector
FMRegressionTrainer.getTarget
(ImmutableOutputInfo<Regressor> outputInfo, Regressor output) Method parameters in org.tribuo.regression.sgd.fm with type arguments of type RegressorModifier and TypeMethodDescriptionprotected FMRegressionModel
FMRegressionTrainer.createModel
(String name, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo, FMParameters parameters) protected DenseVector
FMRegressionTrainer.getTarget
(ImmutableOutputInfo<Regressor> outputInfo, Regressor output) -
Uses of Regressor in org.tribuo.regression.sgd.linear
Subclasses with type arguments of type Regressor in org.tribuo.regression.sgd.linearModifier and TypeClassDescriptionclass
The inference time version of a linear model trained using SGD.class
A trainer for a linear regression model which uses SGD.Methods in org.tribuo.regression.sgd.linear that return types with arguments of type RegressorModifier and TypeMethodDescriptionMethods in org.tribuo.regression.sgd.linear with parameters of type RegressorModifier and TypeMethodDescriptionprotected DenseVector
LinearSGDTrainer.getTarget
(ImmutableOutputInfo<Regressor> outputInfo, Regressor output) Method parameters in org.tribuo.regression.sgd.linear with type arguments of type RegressorModifier and TypeMethodDescriptionprotected LinearSGDModel
LinearSGDTrainer.createModel
(String name, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo, LinearParameters parameters) protected DenseVector
LinearSGDTrainer.getTarget
(ImmutableOutputInfo<Regressor> outputInfo, Regressor output) -
Uses of Regressor in org.tribuo.regression.slm
Classes in org.tribuo.regression.slm that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionclass
An ElasticNet trainer that uses co-ordinate descent.class
A trainer for a sparse linear regression model.Methods in org.tribuo.regression.slm that return types with arguments of type RegressorModifier and TypeMethodDescriptionSparseLinearModel.copy
(String newName, ModelProvenance newProvenance) ElasticNetCDTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) ElasticNetCDTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) Method parameters in org.tribuo.regression.slm with type arguments of type RegressorModifier and TypeMethodDescriptionprotected SparseVector
SparseLinearModel.createFeatures
(Example<Regressor> example) Creates the feature vector.ElasticNetCDTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) ElasticNetCDTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) SLMTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a sparse linear model.SLMTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) Trains a sparse linear model. -
Uses of Regressor in org.tribuo.regression.xgboost
Subclasses with type arguments of type Regressor in org.tribuo.regression.xgboostModifier and TypeClassDescriptionfinal class
ATrainer
which wraps the XGBoost training procedure.Classes in org.tribuo.regression.xgboost that implement interfaces with type arguments of type RegressorModifier and TypeClassDescriptionfinal class
Converts XGBoost outputs intoRegressor
Prediction
s.Methods in org.tribuo.regression.xgboost that return types with arguments of type RegressorModifier and TypeMethodDescriptionXGBoostRegressionConverter.convertBatchOutput
(ImmutableOutputInfo<Regressor> info, List<float[][]> probabilities, int[] numValidFeatures, Example<Regressor>[] examples) XGBoostRegressionConverter.convertOutput
(ImmutableOutputInfo<Regressor> info, List<float[]> probabilities, int numValidFeatures, Example<Regressor> example) XGBoostRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) XGBoostRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) Method parameters in org.tribuo.regression.xgboost with type arguments of type RegressorModifier and TypeMethodDescriptionXGBoostRegressionConverter.convertBatchOutput
(ImmutableOutputInfo<Regressor> info, List<float[][]> probabilities, int[] numValidFeatures, Example<Regressor>[] examples) XGBoostRegressionConverter.convertOutput
(ImmutableOutputInfo<Regressor> info, List<float[]> probabilities, int numValidFeatures, Example<Regressor> example) XGBoostRegressionConverter.convertOutput
(ImmutableOutputInfo<Regressor> info, List<float[]> probabilities, int numValidFeatures, Example<Regressor> example) XGBoostRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) XGBoostRegressionTrainer.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)