Package org.tribuo.evaluation

Class TrainTestSplitter<T extends Output<T>>

java.lang.Object

org.tribuo.evaluation.TrainTestSplitter<T>

Type Parameters:

T - The output type of the examples in the datasource.

public class TrainTestSplitter<T extends Output<T>>
extends Object

Splits data into training and testing sets. Note that this doesn't operate on Dataset, but rather on DataSource.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	TrainTestSplitter.SplitDataSourceProvenance	Provenance for a split data source.

Constructor Summary

Constructors

Constructor	Description
TrainTestSplitter(DataSource<T> data)	Creates a splitter that splits a dataset 70/30 train and test using a default seed.
TrainTestSplitter(DataSource<T> data, double trainProportion, long seed)	Creates a splitter that will split the given data set into a training and testing set.
TrainTestSplitter(DataSource<T> data, long seed)	Creates a splitter that splits a dataset 70/30 train and test.

Method Summary

Modifier and Type	Method	Description
DataSource<T>	getTest()	Gets the testing datasource.
DataSource<T>	getTrain()	Gets the training data source.
int	totalSize()	The total amount of data in train and test combined.

Methods inherited from class java.lang.Object

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

Constructor Details

TrainTestSplitter

public TrainTestSplitter(DataSource<T> data)

Creates a splitter that splits a dataset 70/30 train and test using a default seed.

Parameters:

data - The data to split.

TrainTestSplitter

public TrainTestSplitter(DataSource<T> data,
 long seed)

Creates a splitter that splits a dataset 70/30 train and test.

Parameters:

data - The data to split.

seed - The seed for the RNG.

TrainTestSplitter

public TrainTestSplitter(DataSource<T> data,
 double trainProportion,
 long seed)

Creates a splitter that will split the given data set into a training and testing set. The give proportion of the data will be randomly selected for the training set. The remainder will be in the test set.

Parameters:

data - the data that we want to split.

trainProportion - the proportion of the data to select for training. This should be a number between 0 and 1. For example, a value of 0.7 means that 70% of the data should be selected for the training set.

seed - The seed for the RNG.

Method Details

totalSize

public int totalSize()

The total amount of data in train and test combined.

Returns:

The number of examples.

getTrain

public DataSource<T> getTrain()

Gets the training data source.

Returns:

The training data.

getTest

public DataSource<T> getTest()

Gets the testing datasource.

Returns:

The testing data.