org.tribuo

Class ImmutableDataset<T extends Output<T>>

java.lang.Object

org.tribuo.Dataset<T>

org.tribuo.ImmutableDataset<T>

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.provenance.Provenancable<DatasetProvenance>, Serializable, Iterable<Example<T>>

Direct Known Subclasses:

DatasetView, MinimumCardinalityDataset

public class ImmutableDataset<T extends Output<T>>
extends Dataset<T>
implements Serializable

This is a Dataset which has an ImmutableFeatureMap to store the feature information. Whenever an example is added to this dataset it removes features that do not exist in the FeatureMap. The dataset is immutable after construction (unless the examples are modified).

This class is mostly for performance optimisations inside the framework, and should not generally be used by external code.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected boolean	dropInvalidExamples If true, instead of throwing an exception when an invalid Example is encountered, this Dataset will log a warning and drop it.
protected ImmutableFeatureMap	featureIDMap A map from feature names to IDs for the features found in this dataset.
protected ImmutableOutputInfo<T>	outputIDInfo Output information, and id numbers for outputs found in this dataset.

Fields inherited from class org.tribuo.Dataset

data, indices, outputFactory, sourceProvenance

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	ImmutableDataset(DataProvenance description, OutputFactory<T> outputFactory) If you call this it's your job to setup outputMap, featureIDMap and fill it with examples.
protected	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(DataSource<T> dataSource, FeatureMap featureIDMap, OutputInfo<T> outputIDInfo, boolean dropInvalidExamples) Creates a dataset from a data source.
	ImmutableDataset(DataSource<T> dataSource, Model<T> model, boolean dropInvalidExamples) Creates a dataset from a data source.
	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.

Method Summary

Modifier and Type	Method and Description
protected void	add(Example<T> ex) Adds an Example to the dataset, which will remove features with unknown names.
protected void	add(Example<T> ex, Merger merger) Adds a Example to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids (merging duplicate ids).
static <T extends Output<T>> ImmutableDataset<T>	copyDataset(Dataset<T> dataset) Creates an immutable deep copy of the supplied dataset.
static <T extends Output<T>> ImmutableDataset<T>	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>	copyDataset(Dataset<T> dataset, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, Merger merger) Creates an immutable deep copy of the supplied dataset.
boolean	getDropInvalidExamples() Returns true if this immutable dataset dropped any invalid examples on construction.
ImmutableFeatureMap	getFeatureIDMap() Returns or generates an ImmutableFeatureMap.
ImmutableFeatureMap	getFeatureMap() Returns this dataset's FeatureMap.
ImmutableOutputInfo<T>	getOutputIDInfo() Returns or generates an ImmutableOutputInfo.
ImmutableOutputInfo<T>	getOutputInfo() Returns this dataset's OutputInfo.
Set<T>	getOutputs() Gets the set of outputs that occur in the examples in this dataset.
DatasetProvenance	getProvenance()
static <T extends Output<T>> ImmutableDataset<T>	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.
String	toString()

Methods inherited from class org.tribuo.Dataset

createTransformers, getData, getExample, getOutputFactory, getSourceDescription, getSourceProvenance, iterator, shuffle, size

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface java.lang.Iterable

forEach, spliterator

Field Detail

outputIDInfo

protected ImmutableOutputInfo<T extends Output<T>> outputIDInfo

Output information, and id numbers for outputs found in this dataset.

featureIDMap

protected ImmutableFeatureMap featureIDMap

A map from feature names to IDs for the features found in this dataset.

dropInvalidExamples

protected final boolean dropInvalidExamples

If true, instead of throwing an exception when an invalid Example is encountered, this Dataset will log a warning and drop it.

Constructor Detail

ImmutableDataset

protected ImmutableDataset(DataProvenance description,
                           OutputFactory<T> outputFactory)

If you call this it's your job to setup outputMap, featureIDMap and fill it with examples.

Note: Sets dropInvalidExamples to false.

Parameters:

description - A description of the input data (including preprocessing steps).

outputFactory - The factory for this output type.

ImmutableDataset

public ImmutableDataset(DataSource<T> dataSource,
                        Model<T> model,
                        boolean dropInvalidExamples)

Creates a dataset from a data source. It copies the feature and output maps from the supplied model.

Parameters:

dataSource - The examples.

model - A model to extract feature and output maps from.

dropInvalidExamples - If true, instead of throwing an exception when an invalid Example is encountered, this Dataset will log a warning and drop it.

ImmutableDataset

public ImmutableDataset(DataSource<T> dataSource,
                        FeatureMap featureIDMap,
                        OutputInfo<T> outputIDInfo,
                        boolean dropInvalidExamples)

Creates a dataset from a data source. Creates immutable feature and output maps from the supplied ones.

Parameters:

dataSource - The examples.

featureIDMap - The feature map.

outputIDInfo - The output map.

dropInvalidExamples - If true, instead of throwing an exception when an invalid Example is encountered, this Dataset will log a warning and drop it.

ImmutableDataset

public ImmutableDataset(Iterable<Example<T>> dataSource,
                        DataProvenance description,
                        OutputFactory<T> outputFactory,
                        FeatureMap featureIDMap,
                        OutputInfo<T> outputIDInfo,
                        boolean dropInvalidExamples)

Creates a dataset from a data source. Creates immutable feature and output maps from the supplied ones.

Parameters:

dataSource - The examples.

description - A description of the input data (including preprocessing steps).

outputFactory - The output factory.

featureIDMap - The feature id map, used to remove unknown features.

outputIDInfo - The output id map.

dropInvalidExamples - If true, instead of throwing an exception when an invalid Example is encountered, this Dataset will log a warning and drop it.

ImmutableDataset

public ImmutableDataset(Iterable<Example<T>> dataSource,
                        DataProvenance description,
                        OutputFactory<T> outputFactory,
                        ImmutableFeatureMap featureIDMap,
                        ImmutableOutputInfo<T> outputIDInfo,
                        boolean dropInvalidExamples)

Creates a dataset from a data source.

Parameters:

dataSource - The examples.

description - A description of the input data (including preprocessing steps).

outputFactory - The factory for this output type.

featureIDMap - The feature id map, used to remove unknown features.

outputIDInfo - The output id map.

dropInvalidExamples - If true, instead of throwing an exception when an invalid Example is encountered, this Dataset will log a warning and drop it.

ImmutableDataset

protected 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.

Note: Sets dropInvalidExamples to false.

Parameters:

description - A description of the data you're going to add to this dataset.

outputFactory - The factory for this output type.

featureIDMap - The feature id map, used to remove unknown features.

outputIDInfo - The output id map.

Method Detail

add

protected void add(Example<T> ex)

Adds an Example to the dataset, which will remove features with unknown names.

Parameters:

ex - An Example to add to the dataset.

add

protected void add(Example<T> ex,
                   Merger merger)

Adds a Example to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids (merging duplicate ids).

Parameters:

ex - The example to add.

merger - The Merger to use.

getOutputs

public Set<T> getOutputs()

Description copied from class: Dataset

Gets the set of outputs that occur in the examples in this dataset.

Specified by:

getOutputs in class Dataset<T extends Output<T>>

Returns:

the set of outputs that occur in the examples in this dataset.

getFeatureIDMap

public ImmutableFeatureMap getFeatureIDMap()

Description copied from class: Dataset

Returns or generates an ImmutableFeatureMap.

Specified by:

getFeatureIDMap in class Dataset<T extends Output<T>>

Returns:

An immutable feature map with id numbers.

getFeatureMap

public ImmutableFeatureMap getFeatureMap()

Description copied from class: Dataset

Returns this dataset's FeatureMap.

Specified by:

getFeatureMap in class Dataset<T extends Output<T>>

Returns:

The feature map from this dataset.

getOutputIDInfo

public ImmutableOutputInfo<T> getOutputIDInfo()

Description copied from class: Dataset

Returns or generates an ImmutableOutputInfo.

Specified by:

getOutputIDInfo in class Dataset<T extends Output<T>>

Returns:

An immutable output info.

getOutputInfo

public ImmutableOutputInfo<T> getOutputInfo()

Description copied from class: Dataset

Returns this dataset's OutputInfo.

Specified by:

getOutputInfo in class Dataset<T extends Output<T>>

Returns:

The output info.

getDropInvalidExamples

public boolean getDropInvalidExamples()

Returns true if this immutable dataset dropped any invalid examples on construction.

Returns:

True if it drops invalid examples.

toString

public String toString()

Overrides:

toString in class Dataset<T extends Output<T>>

getProvenance

public DatasetProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<DatasetProvenance>

copyDataset

public static <T extends Output<T>> ImmutableDataset<T> copyDataset(Dataset<T> dataset)

Creates an immutable deep copy of the supplied dataset.

Type Parameters:

T - The type of output.

Parameters:

dataset - The dataset to copy.

Returns:

An immutable copy of the dataset.

copyDataset

public static <T extends Output<T>> ImmutableDataset<T> 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.

Type Parameters:

T - The type of output.

Parameters:

dataset - The dataset to copy.

featureIDMap - The new feature map to use. Removes features which are not found in this map.

outputIDInfo - The new output info to use.

Returns:

An immutable copy of the dataset.

copyDataset

public static <T extends Output<T>> ImmutableDataset<T> copyDataset(Dataset<T> dataset,
                                                                    ImmutableFeatureMap featureIDMap,
                                                                    ImmutableOutputInfo<T> outputIDInfo,
                                                                    Merger merger)

Creates an immutable deep copy of the supplied dataset.

Type Parameters:

T - The type of output.

Parameters:

dataset - The dataset to copy.

featureIDMap - The new feature map to use. Removes features which are not found in this map.

outputIDInfo - The new output info to use.

merger - The merge function to use to reduce features given new ids.

Returns:

An immutable copy of the dataset.

hashFeatureMap

public static <T extends Output<T>> ImmutableDataset<T> 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.

Type Parameters:

T - The type of output.

Parameters:

dataset - The dataset to copy.

hasher - The hashing function to use.

Returns:

An immutable copy of the dataset.