Uses of Class
org.tribuo.Dataset

Packages that use Dataset

Package	Description
org.tribuo	Provides the core interfaces and classes for using Tribuo.
org.tribuo.anomaly.example	Provides anomaly data generators used for demos and testing implementations.
org.tribuo.anomaly.liblinear	Provides an interface to LibLinear-java for anomaly detection problems.
org.tribuo.anomaly.libsvm	Provides an interface to LibSVM for anomaly detection problems.
org.tribuo.classification.baseline	Provides simple baseline multiclass classifiers.
org.tribuo.classification.dtree	Provides implementations of decision trees for classification problems.
org.tribuo.classification.dtree.impl	Provides internal implementation classes for classification decision trees.
org.tribuo.classification.ensemble	Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
org.tribuo.classification.example	Provides a multiclass data generator used for testing implementations, along with several synthetic data generators for 2d binary classification problems to be used in demos or tutorials.
org.tribuo.classification.experiments	Provides a set of main methods for interacting with classification tasks.
org.tribuo.classification.liblinear	Provides an interface to LibLinear-java for classification problems.
org.tribuo.classification.libsvm	Provides an interface to LibSVM for classification problems.
org.tribuo.classification.mnb	Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
org.tribuo.classification.sgd.kernel	Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
org.tribuo.classification.xgboost	Provides an interface to XGBoost for classification problems.
org.tribuo.clustering.example	Provides clustering data generators used for demos and testing implementations.
org.tribuo.clustering.hdbscan	Provides an implementation of HDBSCAN*.
org.tribuo.clustering.kmeans	Provides a multithreaded implementation of K-Means, with a configurable distance function.
org.tribuo.common.liblinear	Provides base classes for using liblinear from Tribuo.
org.tribuo.common.libsvm	The base interface to LibSVM.
org.tribuo.common.nearest	Provides a K-Nearest Neighbours implementation which works across all Tribuo Output types.
org.tribuo.common.sgd	Provides the base classes for models trained with stochastic gradient descent.
org.tribuo.common.tree	Provides common functionality for building decision trees, irrespective of the predicted Output.
org.tribuo.common.xgboost	Provides abstract classes for interfacing with XGBoost abstracting away all the Output dependent parts.
org.tribuo.data	Provides classes for loading in data from disk, processing it into examples, and splitting datasets for things like cross-validation and train-test splits.
org.tribuo.data.csv	Provides classes which can load columnar data (using a RowProcessor) from a CSV (or other character delimited format) file.
org.tribuo.dataset	Provides utility datasets which subsample or otherwise transform the wrapped dataset.
org.tribuo.datasource	Simple data sources for ingesting or aggregating data.
org.tribuo.ensemble	Provides an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
org.tribuo.evaluation	Evaluation base classes, along with code for train/test splits and cross validation.
org.tribuo.evaluation.metrics	This package contains the infrastructure classes for building evaluation metrics.
org.tribuo.hash	Provides the base interface and implementations of the Model hashing which obscures the feature names stored in a model.
org.tribuo.interop.tensorflow	Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported Tribuo output type.
org.tribuo.math.la	Provides a linear algebra system used for numerical operations in Tribuo.
org.tribuo.multilabel.baseline	Provides implementations of binary relevance based multi-label classification algorithms.
org.tribuo.multilabel.ensemble	Provides a multi-label ensemble combiner that performs a (possibly weighted) majority vote among each label independently, along with an implementation of classifier chain ensembles.
org.tribuo.multilabel.example	Provides a multi-label data generator for testing implementations and a configurable data source suitable for demos and tests.
org.tribuo.provenance	Provides Tribuo specific infrastructure for the Provenance system which tracks models and datasets.
org.tribuo.regression.baseline	Provides simple baseline regression predictors.
org.tribuo.regression.example	Provides some example regression data generators for testing implementations.
org.tribuo.regression.impl	Provides skeletal implementations of Regressor Trainer that can wrap a single dimension trainer/model and produce one prediction per dimension independently.
org.tribuo.regression.liblinear	Provides an interface to liblinear for regression problems.
org.tribuo.regression.libsvm	Provides an interface to LibSVM for regression problems.
org.tribuo.regression.rtree	Provides an implementation of decision trees for regression problems.
org.tribuo.regression.rtree.impl	Provides internal implementation classes for the regression trees.
org.tribuo.regression.slm	Provides implementations of sparse linear regression using various forms of regularisation penalty.
org.tribuo.regression.xgboost	Provides an interface to XGBoost for regression problems.
org.tribuo.reproducibility	Reproducibility utility based on Tribuo's provenance objects.
org.tribuo.sequence	Provides core classes for working with sequences of Examples.
org.tribuo.transform	Provides infrastructure for applying transformations to a Dataset.

Uses of Dataset in org.tribuo

Subclasses of Dataset in org.tribuo

Modifier and Type	Class	Description
class	ImmutableDataset<T extends Output<T>>	This is a Dataset which has an ImmutableFeatureMap to store the feature information.
class	MutableDataset<T extends Output<T>>	A MutableDataset is a Dataset with a MutableFeatureMap which grows over time.

Methods in org.tribuo that return Dataset

Modifier and Type	Method	Description
static <T extends Output<T>> Dataset<T>	Dataset.castDataset(Dataset<?> inputDataset, Class<T> outputType)	Casts the dataset to the specified output type, assuming it is valid.

Methods in org.tribuo with parameters of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>> Dataset<T>	Dataset.castDataset(Dataset<?> inputDataset, Class<T> outputType)	Casts the dataset to the specified output type, assuming it is valid.
static <T extends Output<T>> ImmutableDataset<T>	ImmutableDataset.copyDataset(Dataset<T> dataset)	Creates an immutable deep copy of the supplied dataset.
static <T extends Output<T>> ImmutableDataset<T>	ImmutableDataset.copyDataset(Dataset<T> dataset, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo)	Creates an immutable deep copy of the supplied dataset, using a different feature and output map.
static <T extends Output<T>> ImmutableDataset<T>	ImmutableDataset.copyDataset(Dataset<T> dataset, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, Merger merger)	Creates an immutable deep copy of the supplied dataset.
static <T extends Output<T>> MutableDataset<T>	MutableDataset.createDeepCopy(Dataset<T> other)	Creates a deep copy of the supplied Dataset which is mutable.
static <T extends Output<T>> ImmutableDataset<T>	ImmutableDataset.hashFeatureMap(Dataset<T> dataset, Hasher hasher)	Creates an immutable shallow copy of the supplied dataset, using the hasher to generate a HashedFeatureMap which transparently maps from the feature name to the hashed variant.
U	IncrementalTrainer.incrementalTrain(Dataset<T> newData, U model)	Incrementally trains the supplied model with the new data.
List<Prediction<T>>	Model.predict(Dataset<T> examples)	Uses the model to predict the outputs for multiple examples contained in a data set.
default SparseModel<T>	SparseTrainer.train(Dataset<T> examples)	Trains a sparse predictive model using the examples in the given data set.
SparseModel<T>	SparseTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	Trains a sparse predictive model using the examples in the given data set.
default SparseModel<T>	SparseTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)	Trains a predictive model using the examples in the given data set.
default Model<T>	Trainer.train(Dataset<T> examples)	Trains a predictive model using the examples in the given data set.
Model<T>	Trainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	Trains a predictive model using the examples in the given data set.
default Model<T>	Trainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)	Trains a predictive model using the examples in the given data set.

Uses of Dataset in org.tribuo.anomaly.example

Methods in org.tribuo.anomaly.example that return types with arguments of type Dataset

Modifier and Type	Method	Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.denseTrainTest()	Makes a simple dataset for training and testing.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.denseTrainTest()	Makes a simple dataset for training and testing.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.denseTrainTest(double negate)	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.denseTrainTest(double negate)	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.gaussianAnomaly()	Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.gaussianAnomaly()	Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.gaussianAnomaly(long size, double fractionAnomalous)	Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.gaussianAnomaly(long size, double fractionAnomalous)	Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.sparseTrainTest()	Makes a simple dataset for training and testing.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.sparseTrainTest()	Makes a simple dataset for training and testing.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Event>,Dataset<Event>>	AnomalyDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.

Uses of Dataset in org.tribuo.anomaly.liblinear

Methods in org.tribuo.anomaly.liblinear with parameters of type Dataset

Modifier and Type	Method	Description
protected com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]>	LibLinearAnomalyTrainer.extractData(Dataset<Event> data, ImmutableOutputInfo<Event> outputInfo, ImmutableFeatureMap featureMap)

Uses of Dataset in org.tribuo.anomaly.libsvm

Methods in org.tribuo.anomaly.libsvm with parameters of type Dataset

Modifier and Type	Method	Description
protected com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]>	LibSVMAnomalyTrainer.extractData(Dataset<Event> data, ImmutableOutputInfo<Event> outputInfo, ImmutableFeatureMap featureMap)
LibSVMModel<Event>	LibSVMAnomalyTrainer.train(Dataset<Event> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)

Uses of Dataset in org.tribuo.classification.baseline

Methods in org.tribuo.classification.baseline with parameters of type Dataset

Modifier and Type	Method	Description
Model<Label>	DummyClassifierTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)
Model<Label>	DummyClassifierTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount)

Uses of Dataset in org.tribuo.classification.dtree

Methods in org.tribuo.classification.dtree with parameters of type Dataset

Modifier and Type	Method	Description
protected AbstractTrainingNode<Label>	CARTClassificationTrainer.mkTrainingNode(Dataset<Label> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)

Uses of Dataset in org.tribuo.classification.dtree.impl

Constructors in org.tribuo.classification.dtree.impl with parameters of type Dataset

Modifier	Constructor	Description
	ClassifierTrainingNode(LabelImpurity impurity, Dataset<Label> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)	Constructor which creates the inverted file.

Uses of Dataset in org.tribuo.classification.ensemble

Methods in org.tribuo.classification.ensemble with parameters of type Dataset

Modifier and Type	Method	Description
Model<Label>	AdaBoostTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	If the trainer implements WeightedExamples then do boosting by weighting, otherwise do boosting by sampling.
Model<Label>	AdaBoostTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.classification.example

Methods in org.tribuo.classification.example that return types with arguments of type Dataset

Modifier and Type	Method	Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest()	Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest()	Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest(double negate)	Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest(double negate)	Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.denseTrainTest()	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.denseTrainTest()	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.denseTrainTest(double negate)	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.denseTrainTest(double negate)	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.sparseTrainTest()	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.sparseTrainTest()	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.

Uses of Dataset in org.tribuo.classification.experiments

Methods in org.tribuo.classification.experiments that return types with arguments of type Dataset

Modifier and Type	Method	Description
static com.oracle.labs.mlrg.olcut.util.Pair<Model<Label>,Dataset<Label>>	Test.load(Test.ConfigurableTestOptions o)	Loads in the model and the dataset from the options.

Uses of Dataset in org.tribuo.classification.liblinear

Methods in org.tribuo.classification.liblinear with parameters of type Dataset

Modifier and Type	Method	Description
protected com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]>	LibLinearClassificationTrainer.extractData(Dataset<Label> data, ImmutableOutputInfo<Label> outputInfo, ImmutableFeatureMap featureMap)

Uses of Dataset in org.tribuo.classification.libsvm

Methods in org.tribuo.classification.libsvm with parameters of type Dataset

Modifier and Type	Method	Description
protected com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]>	LibSVMClassificationTrainer.extractData(Dataset<Label> data, ImmutableOutputInfo<Label> outputInfo, ImmutableFeatureMap featureMap)

Uses of Dataset in org.tribuo.classification.mnb

Methods in org.tribuo.classification.mnb with parameters of type Dataset

Modifier and Type	Method	Description
Model<Label>	MultinomialNaiveBayesTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
Model<Label>	MultinomialNaiveBayesTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.classification.sgd.kernel

Methods in org.tribuo.classification.sgd.kernel with parameters of type Dataset

Modifier and Type	Method	Description
KernelSVMModel	KernelSVMTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
KernelSVMModel	KernelSVMTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.classification.xgboost

Methods in org.tribuo.classification.xgboost with parameters of type Dataset

Modifier and Type	Method	Description
XGBoostModel<Label>	XGBoostClassificationTrainer.train(Dataset<Label> examples)
XGBoostModel<Label>	XGBoostClassificationTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
XGBoostModel<Label>	XGBoostClassificationTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.clustering.example

Methods in org.tribuo.clustering.example that return Dataset

Modifier and Type	Method	Description
static Dataset<ClusterID>	ClusteringDataGenerator.gaussianClusters(long size, long seed)	Generates a dataset drawn from a mixture of 5 2d gaussians.

Methods in org.tribuo.clustering.example that return types with arguments of type Dataset

Modifier and Type	Method	Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.denseTrainTest()	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.denseTrainTest()	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.denseTrainTest(double negate)	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.denseTrainTest(double negate)	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.sparseTrainTest()	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.sparseTrainTest()	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.

Uses of Dataset in org.tribuo.clustering.hdbscan

Methods in org.tribuo.clustering.hdbscan with parameters of type Dataset

Modifier and Type	Method	Description
HdbscanModel	HdbscanTrainer.train(Dataset<ClusterID> dataset)
HdbscanModel	HdbscanTrainer.train(Dataset<ClusterID> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Uses of Dataset in org.tribuo.clustering.kmeans

Methods in org.tribuo.clustering.kmeans with parameters of type Dataset

Modifier and Type	Method	Description
KMeansModel	KMeansTrainer.train(Dataset<ClusterID> dataset)
KMeansModel	KMeansTrainer.train(Dataset<ClusterID> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
KMeansModel	KMeansTrainer.train(Dataset<ClusterID> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.common.liblinear

Methods in org.tribuo.common.liblinear with parameters of type Dataset

Modifier and Type	Method	Description
protected abstract com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]>	LibLinearTrainer.extractData(Dataset<T> data, ImmutableOutputInfo<T> outputInfo, ImmutableFeatureMap featureMap)	Extracts the features and Outputs in LibLinear's format.
LibLinearModel<T>	LibLinearTrainer.train(Dataset<T> examples)
LibLinearModel<T>	LibLinearTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
LibLinearModel<T>	LibLinearTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.common.libsvm

Methods in org.tribuo.common.libsvm with parameters of type Dataset

Modifier and Type	Method	Description
protected abstract com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]>	LibSVMTrainer.extractData(Dataset<T> data, ImmutableOutputInfo<T> outputInfo, ImmutableFeatureMap featureMap)	Extracts the features and Outputs in LibSVM's format.
LibSVMModel<T>	LibSVMTrainer.train(Dataset<T> examples)
LibSVMModel<T>	LibSVMTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
LibSVMModel<T>	LibSVMTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.common.nearest

Methods in org.tribuo.common.nearest with parameters of type Dataset

Modifier and Type	Method	Description
Model<T>	KNNTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
Model<T>	KNNTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.common.sgd

Methods in org.tribuo.common.sgd with parameters of type Dataset

Modifier and Type	Method	Description
V	AbstractSGDTrainer.train(Dataset<T> examples)
V	AbstractSGDTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
V	AbstractSGDTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.common.tree

Methods in org.tribuo.common.tree with parameters of type Dataset

Modifier and Type	Method	Description
protected abstract AbstractTrainingNode<T>	AbstractCARTTrainer.mkTrainingNode(Dataset<T> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)	Makes the initial training node.
TreeModel<T>	AbstractCARTTrainer.train(Dataset<T> examples)
TreeModel<T>	AbstractCARTTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
TreeModel<T>	AbstractCARTTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.common.xgboost

Methods in org.tribuo.common.xgboost with parameters of type Dataset

Modifier and Type	Method	Description
protected static <T extends Output<T>> XGBoostTrainer.DMatrixTuple<T>	XGBoostTrainer.convertDataset(Dataset<T> examples)
protected static <T extends Output<T>> XGBoostTrainer.DMatrixTuple<T>	XGBoostTrainer.convertDataset(Dataset<T> examples, Function<T,Float> responseExtractor)
List<Prediction<T>>	XGBoostModel.predict(Dataset<T> examples)	Uses the model to predict the labels for multiple examples contained in a data set.

Uses of Dataset in org.tribuo.data

Methods in org.tribuo.data that return types with arguments of type Dataset

Modifier and Type	Method	Description
<T extends Output<T>> com.oracle.labs.mlrg.olcut.util.Pair<Dataset<T>,Dataset<T>>	DataOptions.load(OutputFactory<T> outputFactory)	Loads the training and testing data from DataOptions.trainingPath and DataOptions.testingPath according to the other parameters specified in this class.
<T extends Output<T>> com.oracle.labs.mlrg.olcut.util.Pair<Dataset<T>,Dataset<T>>	DataOptions.load(OutputFactory<T> outputFactory)	Loads the training and testing data from DataOptions.trainingPath and DataOptions.testingPath according to the other parameters specified in this class.

Uses of Dataset in org.tribuo.data.csv

Methods in org.tribuo.data.csv with parameters of type Dataset

Modifier and Type	Method	Description
<T extends Output<T>> void	CSVSaver.save(Path csvPath, Dataset<T> dataset, String responseName)	Saves the dataset to the specified path.
<T extends Output<T>> void	CSVSaver.save(Path csvPath, Dataset<T> dataset, Set<String> responseNames)	Saves the dataset to the specified path.

Uses of Dataset in org.tribuo.dataset

Subclasses of Dataset in org.tribuo.dataset

Modifier and Type	Class	Description
final class	DatasetView<T extends Output<T>>	DatasetView provides an immutable view on another Dataset that only exposes selected examples.
class	MinimumCardinalityDataset<T extends Output<T>>	This class creates a pruned dataset in which low frequency features that occur less than the provided minimum cardinality have been removed.

Methods in org.tribuo.dataset with parameters of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>> DatasetView<T>	DatasetView.createBootstrapView(Dataset<T> dataset, int size, long seed)	Generates a DatasetView bootstrapped from the supplied Dataset.
static <T extends Output<T>> DatasetView<T>	DatasetView.createBootstrapView(Dataset<T> dataset, int size, long seed, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> outputIDs)	Generates a DatasetView bootstrapped from the supplied Dataset.
static <T extends Output<T>> DatasetView<T>	DatasetView.createView(Dataset<T> dataset, Predicate<Example<T>> predicate, String tag)	Creates a view from the supplied dataset, using the specified predicate to test if each example should be in this view.
static <T extends Output<T>> DatasetView<T>	DatasetView.createWeightedBootstrapView(Dataset<T> dataset, int size, long seed, float[] exampleWeights)	Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.
static <T extends Output<T>> DatasetView<T>	DatasetView.createWeightedBootstrapView(Dataset<T> dataset, int size, long seed, float[] exampleWeights, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> outputIDs)	Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.

Constructors in org.tribuo.dataset with parameters of type Dataset

Modifier	Constructor	Description
	DatasetView(Dataset<T> dataset, int[] exampleIndices, String tag)	Creates a DatasetView which includes the supplied indices from the dataset.
	DatasetView(Dataset<T> dataset, int[] exampleIndices, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> labelIDs, String tag)	Creates a DatasetView which includes the supplied indices from the dataset.
	MinimumCardinalityDataset(Dataset<T> dataset, int minCardinality)

Uses of Dataset in org.tribuo.datasource

Methods in org.tribuo.datasource with parameters of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>> void	LibSVMDataSource.writeLibSVMFormat(Dataset<T> dataset, PrintStream out, boolean zeroIndexed, Function<T,Number> transformationFunc)	Writes out a dataset in LibSVM format.

Uses of Dataset in org.tribuo.ensemble

Methods in org.tribuo.ensemble with parameters of type Dataset

Modifier and Type	Method	Description
EnsembleModel<T>	BaggingTrainer.train(Dataset<T> examples)
EnsembleModel<T>	BaggingTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
EnsembleModel<T>	BaggingTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
protected Model<T>	BaggingTrainer.trainSingleModel(Dataset<T> examples, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> labelIDs, int randInt, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)	Trains a single model.

Uses of Dataset in org.tribuo.evaluation

Methods in org.tribuo.evaluation with parameters of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>, C extends MetricContext<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,Double>	EvaluationAggregator.argmax(EvaluationMetric<T,C> metric, List<? extends Model<T>> models, Dataset<T> dataset)	Calculates the argmax of a metric across the supplied models (i.e., the index of the model which performed the best).
final E	AbstractEvaluator.evaluate(Model<T> model, Dataset<T> dataset)	Produces an evaluation for the supplied model and dataset, by calling Model.predict(org.tribuo.Example<T>) to create the predictions, then aggregating the appropriate statistics.
E	Evaluator.evaluate(Model<T> model, Dataset<T> dataset)	Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
Iterator<KFoldSplitter.TrainTestFold<T>>	KFoldSplitter.split(Dataset<T> dataset, boolean shuffle)	Splits a dataset into k consecutive folds; for each fold, the remaining k-1 folds form the training set.
static <T extends Output<T>, C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(List<? extends EvaluationMetric<T,C>> metrics, Model<T> model, Dataset<T> dataset)	Summarize model performance on dataset across several metrics.
static <T extends Output<T>, R extends Evaluation<T>> Map<MetricID<T>,DescriptiveStats>	EvaluationAggregator.summarize(Evaluator<T,R> evaluator, List<? extends Model<T>> models, Dataset<T> dataset)	Summarize performance using the supplied evaluator across several models on one dataset.
static <T extends Output<T>, C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(EvaluationMetric<T,C> metric, List<? extends Model<T>> models, Dataset<T> dataset)	Summarize performance w.r.t.

Method parameters in org.tribuo.evaluation with type arguments of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>, C extends MetricContext<T>> com.oracle.labs.mlrg.olcut.util.Pair<Integer,Double>	EvaluationAggregator.argmax(EvaluationMetric<T,C> metric, Model<T> model, List<? extends Dataset<T>> datasets)	Calculates the argmax of a metric across the supplied datasets.
static <T extends Output<T>, R extends Evaluation<T>> Map<MetricID<T>,DescriptiveStats>	EvaluationAggregator.summarize(Evaluator<T,R> evaluator, Model<T> model, List<? extends Dataset<T>> datasets)	Summarize performance according to evaluator for a single model across several datasets.
static <T extends Output<T>, C extends MetricContext<T>> DescriptiveStats	EvaluationAggregator.summarize(EvaluationMetric<T,C> metric, Model<T> model, List<? extends Dataset<T>> datasets)	Summarize a model's performance w.r.t.

Constructors in org.tribuo.evaluation with parameters of type Dataset

Modifier	Constructor	Description
	CrossValidation(Trainer<T> trainer, Dataset<T> data, Evaluator<T,E> evaluator, int k)	Builds a k-fold cross-validation loop.
	CrossValidation(Trainer<T> trainer, Dataset<T> data, Evaluator<T,E> evaluator, int k, long seed)	Builds a k-fold cross-validation loop.

Uses of Dataset in org.tribuo.evaluation.metrics

Methods in org.tribuo.evaluation.metrics with parameters of type Dataset

Modifier and Type	Method	Description
default C	EvaluationMetric.createContext(Model<T> model, Dataset<T> dataset)	Creates the metric context used to compute this metric's value, generating Predictions for each Example in the supplied dataset.

Uses of Dataset in org.tribuo.hash

Methods in org.tribuo.hash with parameters of type Dataset

Modifier and Type	Method	Description
Model<T>	HashingTrainer.train(Dataset<T> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)	This clones the Dataset, hashes each of the examples and rewrites their feature ids before passing it to the inner trainer.
Model<T>	HashingTrainer.train(Dataset<T> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount)

Uses of Dataset in org.tribuo.interop.tensorflow

Methods in org.tribuo.interop.tensorflow with parameters of type Dataset

Modifier and Type	Method	Description
TensorFlowModel<T>	TensorFlowTrainer.train(Dataset<T> examples)
TensorFlowModel<T>	TensorFlowTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
TensorFlowModel<T>	TensorFlowTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.math.la

Methods in org.tribuo.math.la with parameters of type Dataset

Modifier and Type	Method	Description
static <T extends Output<T>> SparseVector[]	SparseVector.transpose(Dataset<T> dataset)	Converts a dataset of row-major examples into an array of column-major sparse vectors.
static <T extends Output<T>> SparseVector[]	SparseVector.transpose(Dataset<T> dataset, ImmutableFeatureMap fMap)	Converts a dataset of row-major examples into an array of column-major sparse vectors.

Uses of Dataset in org.tribuo.multilabel.baseline

Methods in org.tribuo.multilabel.baseline with parameters of type Dataset

Modifier and Type	Method	Description
ClassifierChainModel	ClassifierChainTrainer.train(Dataset<MultiLabel> examples)
ClassifierChainModel	ClassifierChainTrainer.train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
ClassifierChainModel	ClassifierChainTrainer.train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
Model<MultiLabel>	IndependentMultiLabelTrainer.train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
Model<MultiLabel>	IndependentMultiLabelTrainer.train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.multilabel.ensemble

Methods in org.tribuo.multilabel.ensemble with parameters of type Dataset

Modifier and Type	Method	Description
WeightedEnsembleModel<MultiLabel>	CCEnsembleTrainer.train(Dataset<MultiLabel> examples)
WeightedEnsembleModel<MultiLabel>	CCEnsembleTrainer.train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
WeightedEnsembleModel<MultiLabel>	CCEnsembleTrainer.train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.multilabel.example

Methods in org.tribuo.multilabel.example that return Dataset

Modifier and Type	Method	Description
static Dataset<MultiLabel>	MultiLabelDataGenerator.generateTestData()	Simple test data for checking multi-label trainers.
static Dataset<MultiLabel>	MultiLabelDataGenerator.generateTrainData()	Simple training data for checking multi-label trainers.

Methods in org.tribuo.multilabel.example that return types with arguments of type Dataset

Modifier and Type	Method	Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<MultiLabel>,Dataset<MultiLabel>>	MultiLabelDataGenerator.generateDataset()	Generate training and testing datasets.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<MultiLabel>,Dataset<MultiLabel>>	MultiLabelDataGenerator.generateDataset()	Generate training and testing datasets.

Uses of Dataset in org.tribuo.provenance

Constructors in org.tribuo.provenance with parameters of type Dataset

Modifier	Constructor	Description
	DatasetProvenance(DataProvenance sourceProvenance, com.oracle.labs.mlrg.olcut.provenance.ListProvenance<com.oracle.labs.mlrg.olcut.provenance.ObjectProvenance> transformationProvenance, Dataset<T> dataset)	Creates a dataset provenance from the supplied dataset.

Uses of Dataset in org.tribuo.regression.baseline

Methods in org.tribuo.regression.baseline with parameters of type Dataset

Modifier and Type	Method	Description
DummyRegressionModel	DummyRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)
DummyRegressionModel	DummyRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount)

Uses of Dataset in org.tribuo.regression.example

Methods in org.tribuo.regression.example that return types with arguments of type Dataset

Modifier and Type	Method	Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.denseTrainTest()	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.denseTrainTest()	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	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}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	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}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimDenseTrainTest()	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimDenseTrainTest()	Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	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}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	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}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimSparseTrainTest()	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimSparseTrainTest()	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimSparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.multiDimSparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.sparseTrainTest()	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.sparseTrainTest()	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	RegressionDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	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}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Regressor>,Dataset<Regressor>>	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 Dataset in org.tribuo.regression.impl

Methods in org.tribuo.regression.impl with parameters of type Dataset

Modifier and Type	Method	Description
SkeletalIndependentRegressionSparseModel	SkeletalIndependentRegressionSparseTrainer.train(Dataset<Regressor> examples)
SkeletalIndependentRegressionSparseModel	SkeletalIndependentRegressionSparseTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
SkeletalIndependentRegressionSparseModel	SkeletalIndependentRegressionSparseTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
SkeletalIndependentRegressionModel	SkeletalIndependentRegressionTrainer.train(Dataset<Regressor> examples)
SkeletalIndependentRegressionModel	SkeletalIndependentRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
SkeletalIndependentRegressionModel	SkeletalIndependentRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.regression.liblinear

Methods in org.tribuo.regression.liblinear with parameters of type Dataset

Modifier and Type	Method	Description
protected com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]>	LibLinearRegressionTrainer.extractData(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap)

Uses of Dataset in org.tribuo.regression.libsvm

Methods in org.tribuo.regression.libsvm with parameters of type Dataset

Modifier and Type	Method	Description
protected com.oracle.labs.mlrg.olcut.util.Pair<libsvm.svm_node[][],double[][]>	LibSVMRegressionTrainer.extractData(Dataset<Regressor> data, ImmutableOutputInfo<Regressor> outputInfo, ImmutableFeatureMap featureMap)

Uses of Dataset in org.tribuo.regression.rtree

Methods in org.tribuo.regression.rtree with parameters of type Dataset

Modifier and Type	Method	Description
protected AbstractTrainingNode<Regressor>	CARTJointRegressionTrainer.mkTrainingNode(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)
protected AbstractTrainingNode<Regressor>	CARTRegressionTrainer.mkTrainingNode(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)
TreeModel<Regressor>	CARTRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
TreeModel<Regressor>	CARTRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.regression.rtree.impl

Methods in org.tribuo.regression.rtree.impl with parameters of type Dataset

Modifier and Type	Method	Description
static RegressorTrainingNode.InvertedData	RegressorTrainingNode.invertData(Dataset<Regressor> examples)	Inverts a training dataset from row major to column major.

Constructors in org.tribuo.regression.rtree.impl with parameters of type Dataset

Modifier	Constructor	Description
	JointRegressorTrainingNode(RegressorImpurity impurity, Dataset<Regressor> examples, boolean normalize, AbstractTrainingNode.LeafDeterminer leafDeterminer)	Constructor which creates the inverted file.

Uses of Dataset in org.tribuo.regression.slm

Methods in org.tribuo.regression.slm with parameters of type Dataset

Modifier and Type	Method	Description
SparseModel<Regressor>	ElasticNetCDTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
SparseModel<Regressor>	ElasticNetCDTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
SparseLinearModel	SLMTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	Trains a sparse linear model.
SparseLinearModel	SLMTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)	Trains a sparse linear model.

Uses of Dataset in org.tribuo.regression.xgboost

Methods in org.tribuo.regression.xgboost with parameters of type Dataset

Modifier and Type	Method	Description
XGBoostModel<Regressor>	XGBoostRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
XGBoostModel<Regressor>	XGBoostRegressionTrainer.train(Dataset<Regressor> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)

Uses of Dataset in org.tribuo.reproducibility

Methods in org.tribuo.reproducibility that return Dataset

Modifier and Type	Method	Description
Dataset<T>	ReproUtil.recoverDataset()	Return a Dataset used when a model was trained.

Uses of Dataset in org.tribuo.sequence

Methods in org.tribuo.sequence that return Dataset

Modifier and Type	Method	Description
Dataset<T>	SequenceDataset.getFlatDataset()	Returns a view on this SequenceDataset which aggregates all the examples and ignores the sequence structure.

Uses of Dataset in org.tribuo.transform

Methods in org.tribuo.transform with parameters of type Dataset

Modifier and Type	Method	Description
List<Prediction<T>>	TransformedModel.predict(Dataset<T> examples)
TransformedModel<T>	TransformTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)
TransformedModel<T>	TransformTrainer.train(Dataset<T> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount)
<T extends Output<T>> MutableDataset<T>	TransformerMap.transformDataset(Dataset<T> dataset)	Copies the supplied dataset and applies the transformers to each example in it.
<T extends Output<T>> MutableDataset<T>	TransformerMap.transformDataset(Dataset<T> dataset, boolean densify)	Copies the supplied dataset and applies the transformers to each example in it.