MutableDataset (Tribuo 4.0.2 API)

java.lang.Object
- org.tribuo.Dataset<T>
- - org.tribuo.MutableDataset<T>

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.provenance.Provenancable<DatasetProvenance>, Serializable, Iterable<Example<T>>
```
public class MutableDataset<T extends Output<T>>
extends Dataset<T>
```
A MutableDataset is a Dataset with a MutableFeatureMap which grows over time. Whenever an Example is added to the dataset it observes each feature and output keeping appropriate statistics in the FeatureMap and OutputInfo.

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected boolean`	`dense` Denotes if this dataset contains implicit zeros or not.
`protected MutableFeatureMap`	`featureMap` A map from feature names to feature info objects.
`protected MutableOutputInfo<T>`	`outputMap` Information about the outputs in this dataset.
`protected List<com.oracle.labs.mlrg.olcut.provenance.ObjectProvenance>`	`transformProvenances` The provenances of the transformations applied to this dataset.

Fields inherited from class org.tribuo.Dataset
data, indices, outputFactory, sourceProvenance

Constructor Summary

Constructors
Constructor and Description
`MutableDataset(DataProvenance sourceProvenance, OutputFactory<T> outputFactory)` Creates an empty dataset.
`MutableDataset(DataSource<T> dataSource)` Creates a dataset from a data source.
`MutableDataset(Iterable<Example<T>> dataSource, DataProvenance provenance, OutputFactory<T> outputFactory)` Creates a dataset from a data source.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`add(Example<T> ex)` Adds an example to the dataset, which observes the output and each feature value.
`void`	`addAll(Collection<? extends Example<T>> collection)` Adds all the Examples in the supplied collection to this dataset.
`void`	`clear()` Clears all the examples out of this dataset, and flushes the FeatureMap, OutputInfo, and transform provenances.
`static <T extends Output<T>> MutableDataset<T>`	`createDeepCopy(Dataset<T> other)` Creates a deep copy of the supplied `Dataset` which is mutable.
`void`	`densify()` Iterates through the examples, converting implicit zeros into explicit zeros.
`ImmutableFeatureMap`	`getFeatureIDMap()` Returns or generates an `ImmutableFeatureMap`.
`MutableFeatureMap`	`getFeatureMap()` Returns this dataset's `FeatureMap`.
`ImmutableOutputInfo<T>`	`getOutputIDInfo()` Returns or generates an `ImmutableOutputInfo`.
`OutputInfo<T>`	`getOutputInfo()` Returns this dataset's `OutputInfo`.
`Set<T>`	`getOutputs()` Gets the set of possible outputs in this dataset.
`DatasetProvenance`	`getProvenance()`
`boolean`	`isDense()` Is the dataset dense (i.e., do all features in the domain have a value in each example).
`void`	`setWeights(Map<T,Float> weights)` Sets the weights in each example according to their output.
`String`	`toString()`
`void`	`transform(TransformerMap transformerMap)` Applies all the transformations from the `TransformerMap` to this dataset.

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
  - outputMap
```
protected final MutableOutputInfo<T extends Output<T>> outputMap
```
    Information about the outputs in this dataset.
  - featureMap
```
protected final MutableFeatureMap featureMap
```
    A map from feature names to feature info objects.
  - transformProvenances
```
protected final List<com.oracle.labs.mlrg.olcut.provenance.ObjectProvenance> transformProvenances
```
    The provenances of the transformations applied to this dataset.
  - dense
```
protected boolean dense
```
    Denotes if this dataset contains implicit zeros or not.
- Constructor Detail
  - MutableDataset
```
public MutableDataset(DataProvenance sourceProvenance,
                      OutputFactory<T> outputFactory)
```
    Creates an empty dataset.
    
    Parameters:
    
    sourceProvenance - A description of the input data, including preprocessing steps.
    
    outputFactory - The output factory.
  - MutableDataset
```
public MutableDataset(Iterable<Example<T>> dataSource,
                      DataProvenance provenance,
                      OutputFactory<T> outputFactory)
```
    Creates a dataset from a data source. This method will create the output and feature maps that are needed for training and evaluating classifiers.
    
    Parameters:
    
    dataSource - The examples.
    
    provenance - A description of the input data, including preprocessing steps.
    
    outputFactory - The output factory.
  - MutableDataset
```
public MutableDataset(DataSource<T> dataSource)
```
    Creates a dataset from a data source. This method creates the output and feature maps needed for training and evaluating classifiers.
    
    Parameters:
    
    dataSource - The examples.
- Method Detail
  - add
```
public void add(Example<T> ex)
```
    Adds an example to the dataset, which observes the output and each feature value.
    It also canonicalises the reference to each feature's name (i.e., replacing the reference to a feature's name with the canonical one stored in this Dataset's VariableInfo). This greatly reduces the memory footprint.
    
    Parameters:
    
    ex - The example to add.
  - addAll
```
public void addAll(Collection<? extends Example<T>> collection)
```
    Adds all the Examples in the supplied collection to this dataset.
    
    Parameters:
    
    collection - The collection of Examples.
  - setWeights
```
public void setWeights(Map<T,Float> weights)
```
    Sets the weights in each example according to their output.
    
    Parameters:
    
    weights - A map of Outputs to float weights.
  - getOutputs
```
public Set<T> getOutputs()
```
    Gets the set of possible outputs in this dataset.
    In the case of regression returns a Set containing dimension names.
    
    Specified by:
    
    getOutputs in class Dataset<T extends Output<T>>
    
    Returns:
    
    The set of possible outputs.
  - 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 MutableFeatureMap 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 OutputInfo<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.
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Dataset<T extends Output<T>>
  - isDense
```
public boolean isDense()
```
    Is the dataset dense (i.e., do all features in the domain have a value in each example).
    
    Returns:
    
    True if the dataset is dense.
  - transform
```
public void transform(TransformerMap transformerMap)
```
    Applies all the transformations from the TransformerMap to this dataset.
    
    Parameters:
    
    transformerMap - The transformations to apply.
  - densify
```
public void densify()
```
    Iterates through the examples, converting implicit zeros into explicit zeros.
  - clear
```
public void clear()
```
    Clears all the examples out of this dataset, and flushes the FeatureMap, OutputInfo, and transform provenances.
  - getProvenance
```
public DatasetProvenance getProvenance()
```
  - createDeepCopy
```
public static <T extends Output<T>> MutableDataset<T> createDeepCopy(Dataset<T> other)
```
    Creates a deep copy of the supplied Dataset which is mutable.
    Copies the individual examples using their copy method.
    
    Type Parameters:
    
    T - The output type.
    
    Parameters:
    
    other - The dataset to copy.
    
    Returns:
    
    A mutable deep copy of the dataset.

Class MutableDataset<T extends Output<T>>

Field Summary

Fields inherited from class org.tribuo.Dataset

Constructor Summary

Method Summary

Methods inherited from class org.tribuo.Dataset

Methods inherited from class java.lang.Object

Methods inherited from interface java.lang.Iterable

Field Detail

outputMap

featureMap

transformProvenances

dense

Constructor Detail

MutableDataset

MutableDataset

MutableDataset

Method Detail

add

addAll

setWeights

getOutputs

getFeatureIDMap

getFeatureMap

getOutputIDInfo

getOutputInfo

toString

isDense

transform

densify

clear

getProvenance

createDeepCopy