Package org.apache.lucene.classification.utils

Class DatasetSplitter

java.lang.Object

org.apache.lucene.classification.utils.DatasetSplitter

public class DatasetSplitter
extends Object

Utility class for creating training / test / cross validation indexes from the original index.

Constructor Summary

Constructors

Constructor	Description
DatasetSplitter(double testRatio, double crossValidationRatio)	Create a DatasetSplitter by giving test and cross validation IDXs sizes

Method Summary

Modifier and Type	Method	Description
void	split(IndexReader originalIndex, Directory trainingIndex, Directory testIndex, Directory crossValidationIndex, Analyzer analyzer, boolean termVectors, String classFieldName, String... fieldNames)	Split a given index into 3 indexes for training, test and cross validation tasks respectively

Methods inherited from class java.lang.Object

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

Constructor Details

DatasetSplitter

public DatasetSplitter(double testRatio,
 double crossValidationRatio)

Create a DatasetSplitter by giving test and cross validation IDXs sizes

Parameters:

testRatio - the ratio of the original index to be used for the test IDX as a double between 0.0 and 1.0

crossValidationRatio - the ratio of the original index to be used for the c.v. IDX as a double between 0.0 and 1.0

Method Details

split

public void split(IndexReader originalIndex,
 Directory trainingIndex,
 Directory testIndex,
 Directory crossValidationIndex,
 Analyzer analyzer,
 boolean termVectors,
 String classFieldName,
 String... fieldNames)
           throws IOException

Split a given index into 3 indexes for training, test and cross validation tasks respectively

Parameters:

originalIndex - an LeafReader on the source index

trainingIndex - a Directory used to write the training index

testIndex - a Directory used to write the test index

crossValidationIndex - a Directory used to write the cross validation index

analyzer - Analyzer used to create the new docs

termVectors - true if term vectors should be kept

classFieldName - name of the field used as the label for classification; this must be indexed with sorted doc values

fieldNames - names of fields that need to be put in the new indexes or null if all should be used

Throws:

IOException - if any writing operation fails on any of the indexes