Package | Description |
---|---|
org.tribuo |
Provides the core interfaces and classes for using Tribuo.
|
org.tribuo.anomaly |
Provides classes and infrastructure for anomaly detection problems.
|
org.tribuo.classification |
Provides classes and infrastructure for multiclass classification problems.
|
org.tribuo.classification.explanations.lime |
Provides an implementation of LIME (Locally Interpretable Model Explanations).
|
org.tribuo.clustering |
Provides classes and infrastructure for working with clustering problems.
|
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.columnar |
Provides classes for processing columnar data and generating
Example s. |
org.tribuo.data.columnar.processors.response |
Provides implementations of
ResponseProcessor . |
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.data.sql |
Provides classes which can load columnar data (using a
RowProcessor )
from a SQL source. |
org.tribuo.data.text | |
org.tribuo.data.text.impl |
Provides implementations of text data processors.
|
org.tribuo.datasource |
Simple data sources for ingesting or aggregating data.
|
org.tribuo.interop |
This package contains the abstract implementation of an external model
trained by something outside of Tribuo.
|
org.tribuo.interop.onnx |
This package contains a Tribuo wrapper around the ONNX Runtime.
|
org.tribuo.interop.onnx.extractors |
Provides feature extraction implementations which use ONNX models.
|
org.tribuo.interop.tensorflow |
Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported
Tribuo output type.
|
org.tribuo.multilabel |
Provides classes and infrastructure for working with multi-label classification problems.
|
org.tribuo.provenance |
Provides Tribuo specific infrastructure for the
Provenance system which
tracks models and datasets. |
org.tribuo.regression |
Provides classes and infrastructure for regression problems with single or multiple output dimensions.
|
org.tribuo.regression.example |
Provides some example regression data generators for testing implementations.
|
org.tribuo.sequence |
Provides core classes for working with sequences of
Example s. |
Modifier and Type | Field and Description |
---|---|
protected OutputFactory<T> |
Dataset.outputFactory
A factory for making
OutputInfo and Output of the appropriate type. |
Modifier and Type | Method and Description |
---|---|
OutputFactory<T> |
Dataset.getOutputFactory()
Gets the output factory this dataset contains.
|
OutputFactory<T> |
DataSource.getOutputFactory()
Returns the OutputFactory associated with this Output subclass.
|
Constructor and Description |
---|
Dataset(DataProvenance provenance,
OutputFactory<T> outputFactory)
Creates a dataset.
|
ImmutableDataset(DataProvenance description,
OutputFactory<T> outputFactory)
If you call this it's your job to setup outputMap, featureIDMap and fill it with examples.
|
ImmutableDataset(DataProvenance description,
OutputFactory<T> outputFactory,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo)
This is dangerous, and should not be used unless you've overridden everything in ImmutableDataset.
|
ImmutableDataset(Iterable<Example<T>> dataSource,
DataProvenance description,
OutputFactory<T> outputFactory,
FeatureMap featureIDMap,
OutputInfo<T> outputIDInfo,
boolean dropInvalidExamples)
Creates a dataset from a data source.
|
ImmutableDataset(Iterable<Example<T>> dataSource,
DataProvenance description,
OutputFactory<T> outputFactory,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
boolean dropInvalidExamples)
Creates a dataset from a data source.
|
MutableDataset(DataProvenance sourceProvenance,
OutputFactory<T> outputFactory)
Creates an empty dataset.
|
MutableDataset(Iterable<Example<T>> dataSource,
DataProvenance provenance,
OutputFactory<T> outputFactory)
Creates a dataset from a data source.
|
Modifier and Type | Class and Description |
---|---|
class |
AnomalyFactory
A factory for generating events.
|
Modifier and Type | Class and Description |
---|---|
class |
LabelFactory
A factory for making Label related classes.
|
Modifier and Type | Field and Description |
---|---|
protected static OutputFactory<Regressor> |
LIMEBase.regressionFactory |
Modifier and Type | Class and Description |
---|---|
class |
ClusteringFactory
A factory for making ClusterID related classes.
|
Modifier and Type | Method and Description |
---|---|
static <T extends Output<T>> |
XGBoostExternalModel.createXGBoostModel(OutputFactory<T> factory,
Map<String,Integer> featureMapping,
Map<T,Integer> outputMapping,
XGBoostOutputConverter<T> outputFunc,
ml.dmlc.xgboost4j.java.Booster model,
Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)
Creates an
XGBoostExternalModel from the supplied in-memory XGBoost Booster . |
static <T extends Output<T>> |
XGBoostExternalModel.createXGBoostModel(OutputFactory<T> factory,
Map<String,Integer> featureMapping,
Map<T,Integer> outputMapping,
XGBoostOutputConverter<T> outputFunc,
ml.dmlc.xgboost4j.java.Booster model,
URL provenanceLocation)
Deprecated.
As the URL argument must always be valid. To wrap an in-memory booster use
XGBoostExternalModel.createXGBoostModel(OutputFactory, Map, Map, XGBoostOutputConverter, Booster, Map) . |
static <T extends Output<T>> |
XGBoostExternalModel.createXGBoostModel(OutputFactory<T> factory,
Map<String,Integer> featureMapping,
Map<T,Integer> outputMapping,
XGBoostOutputConverter<T> outputFunc,
Path path)
Creates an
XGBoostExternalModel from the supplied model on disk. |
static <T extends Output<T>> |
XGBoostExternalModel.createXGBoostModel(OutputFactory<T> factory,
Map<String,Integer> featureMapping,
Map<T,Integer> outputMapping,
XGBoostOutputConverter<T> outputFunc,
String path)
Creates an
XGBoostExternalModel from the supplied model on disk. |
Modifier and Type | Field and Description |
---|---|
OutputFactory<?> |
ConfigurableTrainTest.ConfigurableTrainTestOptions.outputFactory |
Modifier and Type | Method and Description |
---|---|
<T extends Output<T>> |
DataOptions.load(OutputFactory<T> outputFactory) |
Modifier and Type | Method and Description |
---|---|
OutputFactory<T> |
ResponseProcessor.getOutputFactory()
Gets the OutputFactory this ResponseProcessor uses.
|
OutputFactory<T> |
ColumnarDataSource.getOutputFactory() |
Constructor and Description |
---|
ColumnarDataSource(OutputFactory<T> outputFactory,
RowProcessor<T> rowProcessor,
boolean outputRequired)
Constructs a columnar data source with the specified parameters.
|
Modifier and Type | Method and Description |
---|---|
OutputFactory<T> |
QuartileResponseProcessor.getOutputFactory() |
OutputFactory<T> |
FieldResponseProcessor.getOutputFactory() |
OutputFactory<T> |
EmptyResponseProcessor.getOutputFactory() |
OutputFactory<T> |
BinaryResponseProcessor.getOutputFactory() |
Constructor and Description |
---|
BinaryResponseProcessor(String fieldName,
String positiveResponse,
OutputFactory<T> outputFactory)
Constructs a binary response processor which emits a positive value for a single string
and a negative value for all other field values.
|
EmptyResponseProcessor(OutputFactory<T> outputFactory)
Constructs a response processor which never emits a response.
|
FieldResponseProcessor(String fieldName,
String defaultValue,
OutputFactory<T> outputFactory)
Constructs a response processor which passes the field value through the
output factory.
|
QuartileResponseProcessor(String name,
String fieldName,
Quartile quartile,
OutputFactory<T> outputFactory)
Constructs a repsonse processor which emits 4 distinct bins for the output factory to process.
|
Constructor and Description |
---|
CSVLoader(char separator,
char quote,
OutputFactory<T> outputFactory)
Creates a CSVLoader using the supplied separator, quote and output factory.
|
CSVLoader(char separator,
OutputFactory<T> outputFactory)
Creates a CSVLoader using the supplied separator and output factory.
|
CSVLoader(OutputFactory<T> outputFactory)
Creates a CSVLoader using the supplied output factory.
|
Constructor and Description |
---|
SQLDataSource(String sqlString,
SQLDBConfig sqlConfig,
OutputFactory<T> outputFactory,
RowProcessor<T> rowProcessor,
boolean outputRequired) |
Modifier and Type | Field and Description |
---|---|
protected OutputFactory<T> |
TextDataSource.outputFactory
The factory that converts a String into an
Output . |
protected OutputFactory<T> |
DirectoryFileSource.outputFactory
The factory that converts a String into an
Output . |
Modifier and Type | Method and Description |
---|---|
OutputFactory<T> |
TextDataSource.getOutputFactory()
Returns the output factory used to convert the text input into an
Output . |
OutputFactory<T> |
DirectoryFileSource.getOutputFactory() |
Constructor and Description |
---|
DirectoryFileSource(OutputFactory<T> outputFactory,
TextFeatureExtractor<T> extractor,
DocumentPreprocessor... preprocessors)
Creates a data source that will use the given feature extractor and
document preprocessors on the data read from the files in the directories
representing classes.
|
DirectoryFileSource(Path newsDir,
OutputFactory<T> outputFactory,
TextFeatureExtractor<T> extractor,
DocumentPreprocessor... preprocessors) |
TextDataSource(File file,
OutputFactory<T> outputFactory,
TextFeatureExtractor<T> extractor,
DocumentPreprocessor... preprocessors) |
TextDataSource(Path path,
OutputFactory<T> outputFactory,
TextFeatureExtractor<T> extractor,
DocumentPreprocessor... preprocessors)
Creates a text data set by reading it from a path.
|
Constructor and Description |
---|
SimpleStringDataSource(List<String> rawLines,
OutputFactory<T> outputFactory,
TextFeatureExtractor<T> extractor) |
SimpleTextDataSource(File file,
OutputFactory<T> outputFactory,
TextFeatureExtractor<T> extractor) |
SimpleTextDataSource(OutputFactory<T> outputFactory,
TextFeatureExtractor<T> extractor) |
SimpleTextDataSource(Path path,
OutputFactory<T> outputFactory,
TextFeatureExtractor<T> extractor) |
Modifier and Type | Method and Description |
---|---|
OutputFactory<T> |
ListDataSource.getOutputFactory() |
OutputFactory<T> |
LibSVMDataSource.getOutputFactory() |
OutputFactory<T> |
IDXDataSource.getOutputFactory() |
OutputFactory<T> |
AggregateDataSource.getOutputFactory() |
OutputFactory<T> |
AggregateConfigurableDataSource.getOutputFactory() |
Constructor and Description |
---|
IDXDataSource(Path featuresPath,
Path outputPath,
OutputFactory<T> outputFactory)
Constructs an IDXDataSource from the supplied paths.
|
LibSVMDataSource(Path path,
OutputFactory<T> outputFactory)
Constructs a LibSVMDataSource from the supplied path and output factory.
|
LibSVMDataSource(Path path,
OutputFactory<T> outputFactory,
boolean zeroIndexed,
int maxFeatureID)
Constructs a LibSVMDataSource from the supplied path and output factory.
|
LibSVMDataSource(URL url,
OutputFactory<T> outputFactory)
Constructs a LibSVMDataSource from the supplied URL and output factory.
|
LibSVMDataSource(URL url,
OutputFactory<T> outputFactory,
boolean zeroIndexed,
int maxFeatureID)
Constructs a LibSVMDataSource from the supplied URL and output factory.
|
ListDataSource(List<Example<T>> list,
OutputFactory<T> factory,
DataSourceProvenance provenance) |
Modifier and Type | Method and Description |
---|---|
protected static <T extends Output<T>> |
ExternalModel.createOutputInfo(OutputFactory<T> factory,
Map<T,Integer> outputs)
Creates an output info from a set of outputs.
|
Constructor and Description |
---|
ExternalDatasetProvenance(String description,
OutputFactory<T> factory,
boolean isSequence,
int numFeatures,
int numOutputs) |
Modifier and Type | Method and Description |
---|---|
static <T extends Output<T>> |
ONNXExternalModel.createOnnxModel(OutputFactory<T> factory,
Map<String,Integer> featureMapping,
Map<T,Integer> outputMapping,
ExampleTransformer featureTransformer,
OutputTransformer<T> outputTransformer,
ai.onnxruntime.OrtSession.SessionOptions opts,
Path path,
String inputName)
Creates an
ONNXExternalModel by loading the model from disk. |
static <T extends Output<T>> |
ONNXExternalModel.createOnnxModel(OutputFactory<T> factory,
Map<String,Integer> featureMapping,
Map<T,Integer> outputMapping,
ExampleTransformer featureTransformer,
OutputTransformer<T> outputTransformer,
ai.onnxruntime.OrtSession.SessionOptions opts,
String filename,
String inputName)
Creates an
ONNXExternalModel by loading the model from disk. |
Constructor and Description |
---|
BERTFeatureExtractor(OutputFactory<T> outputFactory,
Path modelPath,
Path tokenizerPath)
Constructs a BERTFeatureExtractor.
|
BERTFeatureExtractor(OutputFactory<T> outputFactory,
Path modelPath,
Path tokenizerPath,
BERTFeatureExtractor.OutputPooling pooling,
int maxLength,
boolean useCUDA)
Constructs a BERTFeatureExtractor.
|
Modifier and Type | Method and Description |
---|---|
static <T extends Output<T>> |
TensorFlowSavedModelExternalModel.createTensorflowModel(OutputFactory<T> factory,
Map<String,Integer> featureMapping,
Map<T,Integer> outputMapping,
String outputName,
FeatureConverter featureConverter,
OutputConverter<T> outputConverter,
String bundleDirectory)
Creates a TensorflowSavedModelExternalModel by loading in a
SavedModelBundle . |
static <T extends Output<T>> |
TensorFlowFrozenExternalModel.createTensorflowModel(OutputFactory<T> factory,
Map<String,Integer> featureMapping,
Map<T,Integer> outputMapping,
String inputName,
String outputName,
FeatureConverter featureConverter,
OutputConverter<T> outputConverter,
String filename)
Creates a TensorflowFrozenExternalModel by loading in a frozen graph.
|
Modifier and Type | Class and Description |
---|---|
class |
MultiLabelFactory
A factory for generating MultiLabel objects and their associated OutputInfo and Evaluator objects.
|
Constructor and Description |
---|
SimpleDataSourceProvenance(String description,
OffsetDateTime creationTime,
OutputFactory<T> outputFactory)
This constructor initialises the provenance using the supplied description, time and output factory.
|
SimpleDataSourceProvenance(String description,
OutputFactory<T> outputFactory)
This constructor initialises the provenance using the current time in the system timezone.
|
Modifier and Type | Class and Description |
---|---|
class |
RegressionFactory
A factory for creating
Regressor s and RegressionInfo s. |
Modifier and Type | Method and Description |
---|---|
OutputFactory<Regressor> |
NonlinearGaussianDataSource.getOutputFactory() |
OutputFactory<Regressor> |
GaussianDataSource.getOutputFactory() |
Modifier and Type | Field and Description |
---|---|
protected OutputFactory<T> |
SequenceDataset.outputFactory
A factory for making
OutputInfo and Output of the appropriate type. |
Modifier and Type | Method and Description |
---|---|
OutputFactory<T> |
SequenceDataset.getOutputFactory()
Gets the output factory.
|
OutputFactory<T> |
SequenceDataSource.getOutputFactory()
Gets the OutputFactory which was used to generate the Outputs in this SequenceDataSource.
|
Modifier and Type | Method and Description |
---|---|
static <T extends Output<T>> |
SequenceExample.createWithEmptyOutputs(List<? extends List<? extends Feature>> features,
OutputFactory<T> outputFactory)
Creates a SequenceExample using
getUnknownOutput() as the output for each
sequence element. |
Constructor and Description |
---|
ImmutableSequenceDataset(DataProvenance sourceProvenance,
OutputFactory<T> outputFactory)
If you call this it's your job to setup outputIDInfo and featureIDMap.
|
ImmutableSequenceDataset(Iterable<SequenceExample<T>> dataSource,
DataProvenance sourceProvenance,
FeatureMap featureIDMap,
OutputInfo<T> outputIDInfo,
OutputFactory<T> outputFactory)
Creates a dataset from a data source.
|
ImmutableSequenceDataset(Iterable<SequenceExample<T>> dataSource,
DataProvenance sourceProvenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
OutputFactory<T> outputFactory)
Creates a dataset from a data source.
|
MutableSequenceDataset(DataProvenance sourceProvenance,
OutputFactory<T> outputFactory)
Creates an empty sequence dataset.
|
MutableSequenceDataset(Iterable<SequenceExample<T>> dataSource,
DataProvenance sourceProvenance,
OutputFactory<T> outputFactory)
Creates a dataset from a data source.
|
SequenceDataset(DataProvenance sourceProvenance,
OutputFactory<T> outputFactory) |
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