net.librec.data.splitter.GivenNDataSplitter Maven / Gradle / Ivy
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/**
* Copyright (C) 2016 LibRec
*
* This file is part of LibRec.
* LibRec is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* LibRec is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LibRec. If not, see
* Split dataset into train set and test set by given number.
*
* @author WangYuFeng, liuxz and Keqiang Wang
*/
public class GivenNDataSplitter extends AbstractDataSplitter {
/**
* The rate dataset for splitting
*/
private SequentialAccessSparseMatrix preferenceMatrix;
/**
* The datetime dataset for splitting
*/
private SequentialAccessSparseMatrix datetimeMatrix;
/**
* Empty constructor.
*/
public GivenNDataSplitter() {
}
/**
* Initializes a newly created {@code GivenNDataSplitter} object
* with configuration.
*
* @param dataConvertor data convertor
* @param conf the configuration for the splitter.
*/
public GivenNDataSplitter(DataConvertor dataConvertor, Configuration conf) {
this.dataConvertor = dataConvertor;
this.conf = conf;
}
/**
* Split the data.
*
* @throws LibrecException if error occurs
*/
@Override
public void splitData() throws LibrecException {
if (null == this.preferenceMatrix) {
// if (Objects.equals(conf.get("data.convert.columns"), null)) {
// this.preferenceMatrix = dataConvertor.getPreferenceMatrix();
// } else {
// this.preferenceMatrix = dataConvertor.getPreferenceMatrix(conf.get("data.convert.columns").split(","));
// }
this.preferenceMatrix = dataConvertor.getPreferenceMatrix(conf);
if ((!(dataConvertor instanceof ArffDataConvertor))
&&(StringUtils.equals(conf.get("data.column.format"), "UIRT"))) {
this.datetimeMatrix = dataConvertor.getDatetimeMatrix();
}
}
String splitter = conf.get("data.splitter.givenn");
switch (splitter.toLowerCase()) {
case "user": {
try {
getGivenNByUser(Integer.parseInt(conf.get("data.splitter.givenn.n")));
} catch (Exception e) {
e.printStackTrace();
}
break;
}
case "item": {
try {
getGivenNByItem(Integer.parseInt(conf.get("data.splitter.givenn.n")));
} catch (Exception e) {
e.printStackTrace();
}
break;
}
case "userdate": {
try {
getGivenNByUserDate(Integer.parseInt(conf.get("data.splitter.givenn.n")));
} catch (Exception e) {
e.printStackTrace();
}
break;
}
case "itemdate": {
try {
getGivenNByItemDate(Integer.parseInt(conf.get("data.splitter.givenn.n")));
} catch (Exception e) {
e.printStackTrace();
}
break;
}
default:
break;
}
}
/**
* Split ratings into two parts: the training set consisting of user-item
* ratings where {@code numGiven} ratings are preserved for each user, and
* the rest are used as the testing data.
*
* @param numGiven given number
* @throws Exception if error occurs
*/
public void getGivenNByUser(int numGiven) throws Exception {
if (numGiven > 0) {
trainMatrix = new SequentialAccessSparseMatrix(preferenceMatrix);
testMatrix = new SequentialAccessSparseMatrix(preferenceMatrix);
for (int rowIndex = 0, rowSize = preferenceMatrix.rowSize(); rowIndex < rowSize; rowIndex++) {
int numRated = preferenceMatrix.row(rowIndex).getNumEntries();
if (numRated > numGiven) {
int[] givenPositions = Randoms.nextIntArray(numGiven, numRated);
for (int testColumnPosition = 0, columnPosition = 0; columnPosition < numRated; columnPosition++) {
if (testColumnPosition < givenPositions.length && givenPositions[testColumnPosition] == columnPosition) {
testMatrix.setAtColumnPosition(rowIndex, columnPosition, 0.0D);
testColumnPosition++;
} else {
trainMatrix.setAtColumnPosition(rowIndex, columnPosition, 0.0D);
}
}
} else {
for (Vector.VectorEntry vectorEntry : preferenceMatrix.row(rowIndex)) {
testMatrix.setAtColumnPosition(rowIndex, vectorEntry.position(), 0.0D);
}
}
}
trainMatrix.reshape();
testMatrix.reshape();
}
}
/**
* Split ratings into two parts: the training set consisting of user-item
* ratings where {@code numGiven} earliest ratings are preserved for each
* user, and the rest are used as the testing data.
*
* @param numGiven given number
*/
public void getGivenNByUserDate(int numGiven) {
if (numGiven > 0) {
trainMatrix = new SequentialAccessSparseMatrix(preferenceMatrix);
testMatrix = new SequentialAccessSparseMatrix(preferenceMatrix);
for (int rowIndex = 0, rowSize = preferenceMatrix.rowSize(); rowIndex < rowSize; rowIndex++) {
SequentialSparseVector itemRatingVector = preferenceMatrix.row(rowIndex);
if (itemRatingVector.getNumEntries() < 1) {
continue;
}
List itemRatingList = new ArrayList<>(itemRatingVector.getNumEntries());
for (Vector.VectorEntry vectorEntry : itemRatingVector) {
itemRatingList.add(new RatingContext(rowIndex, vectorEntry.position(), (long) vectorEntry.get()));
}
Collections.sort(itemRatingList);
for (int index = 0; index < itemRatingList.size(); index++) {
if (index < numGiven) {
testMatrix.setAtColumnPosition(rowIndex, itemRatingList.get(index).getItem(), 0.0D);
} else {
trainMatrix.setAtColumnPosition(rowIndex, itemRatingList.get(index).getItem(), 0.0D);
}
}
}
trainMatrix.reshape();
testMatrix.reshape();
}
}
/**
* Split ratings into two parts: the training set consisting of user-item
* ratings where {@code numGiven} ratings are preserved for each item, and
* the rest are used as the testing data.
*
* @param numGiven given number
* @throws Exception if error occurs
*/
public void getGivenNByItem(int numGiven) throws Exception {
if (numGiven > 0) {
trainMatrix = new SequentialAccessSparseMatrix(preferenceMatrix);
testMatrix = new SequentialAccessSparseMatrix(preferenceMatrix);
for (int columnIndex = 0, columnSize = preferenceMatrix.columnSize(); columnIndex < columnSize; columnIndex++) {
int numRated = preferenceMatrix.column(columnIndex).getNumEntries();
if (numRated > numGiven) {
int[] givenPositions = Randoms.nextIntArray(numGiven, numRated);
for (int testRowPosition = 0, rowPosition = 0; rowPosition < numRated; rowPosition++) {
if (testRowPosition < givenPositions.length && givenPositions[testRowPosition] == rowPosition) {
testMatrix.setAtRowPosition(rowPosition, columnIndex, 0.0D);
testRowPosition++;
} else {
trainMatrix.setAtRowPosition(rowPosition, columnIndex, 0.0D);
}
}
} else {
for (int rowPosition = 0; rowPosition < numRated; rowPosition++) {
testMatrix.setAtRowPosition(rowPosition, columnIndex, 0.0D);
}
}
}
trainMatrix.reshape();
testMatrix.reshape();
}
}
/**
* Split ratings into two parts: the training set consisting of user-item
* ratings where {@code numGiven} earliest ratings are preserved for each
* item, and the rest are used as the testing data.
*
* @param numGiven given number
*/
public void getGivenNByItemDate(int numGiven) {
if (numGiven > 0) {
trainMatrix = new SequentialAccessSparseMatrix(preferenceMatrix);
testMatrix = new SequentialAccessSparseMatrix(preferenceMatrix);
for (int columnIndex = 0, columnSize = preferenceMatrix.columnSize(); columnIndex < columnSize; columnIndex++) {
SequentialSparseVector userRatingVector = preferenceMatrix.column(columnIndex);
if (userRatingVector.getNumEntries() < 1) {
continue;
}
List ratingContexts = new ArrayList<>(userRatingVector.getNumEntries());
for (Vector.VectorEntry vectorEntry : userRatingVector) {
ratingContexts.add(new RatingContext(vectorEntry.position(), columnIndex,
(long) datetimeMatrix.getAtRowPosition(vectorEntry.position(), columnIndex)));
}
Collections.sort(ratingContexts);
for (int rowPosition = 0; rowPosition < ratingContexts.size(); rowPosition++) {
RatingContext ratingContext = ratingContexts.get(rowPosition);
if (rowPosition < numGiven)
testMatrix.setAtRowPosition(ratingContext.getUser(), columnIndex, 0.0D);
else
trainMatrix.setAtRowPosition(ratingContext.getUser(), columnIndex, 0.0D);
}
}
trainMatrix.reshape();
testMatrix.reshape();
}
}
}