net.librec.ensemble.Ensemble 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 weights;
/**
*
*/
public List evaluationList;
/**
*
*/
public List namesOfAlg;
/**
*
*/
//public List> recommendedItemListOfAlgs;
public List recommendedItemListOfAlgs;
/**
*
*/
//public List> recommendedItemListResult;
public List recommendedItemListResult;
/**
*
*/
public List recommendedItemFinal;
/**
*
*/
public List validationList;
/**
*
*/
public List recommendedItemListFilter;
/**
*
*/
public int topN;
/**
*
*/
public DataModel dataModel;
/**
*
*/
public List realRating;
/**
*
*/
protected final Log LOG = LogFactory.getLog(RecommenderJob.class);
/**
* @param configFile
* @throws Exception
*/
public Ensemble(String configFile) throws Exception {
this.configFile = configFile;
this.weights = new ArrayList();
this.evaluationList = new ArrayList();
this.namesOfAlg = new ArrayList();
this.recommendedItemListOfAlgs = new ArrayList();
this.recommendedItemListResult = new ArrayList();
this.validationList = new ArrayList>();
this.recommendedItemListFilter = new ArrayList>();
this.recommendedItemFinal = new ArrayList();
this.realRating = new ArrayList();
this.conf = configInfo(configFile);
//Configuration conff = new Configuration();
//Configuration.Resource resource = new Configuration.Resource(configFile);
//conff.addResource(resource);
//this.conf = conff;
this.numsOfAlg = conf.getInt("rec.ensemble.numsOfAlg");
this.isranking = conf.get("rec.recommender.isranking");
this.topN = conf.getInt("rec.recommender.topN");
this.algClass = conf.get("rec.recommender.class");
}
/**
* @throws Exception
*/
protected void trainModel() throws Exception {
this.dataModel = dataModel(this.conf);
trainDataRealRating(this.dataModel);
int tempNums = this.numsOfAlg;
// individual newRecommemder models
while (tempNums > 0) {
String algNum = String.valueOf(this.numsOfAlg - tempNums);
double weightTemp = this.conf.getDouble("rec.ensemble.weight." + algNum);
this.weights.add(weightTemp);
tempNums--;
}
}
/**
*
* @param configFile
* @return
* @throws Exception
*/
// protected RecommenderContext prenewRecommemder(String configFile) throws Exception{
// Configuration confAlg = configInfo(configFile);
// String algClass = confAlg.get("rec.recommender.fullClassName");
// this.namesOfAlg.plus(algClass);
// // Use the same dataModel
// RecommenderContext context = this.context(confAlg, this.dataModel);
// return context;
// }
/**
* Recommender configuration
*
* @param sourceFile
* @return
* @throws LibrecException
*/
protected Configuration configInfo(String sourceFile) throws LibrecException {
//this.conf = configInfo(configFile);
Configuration conff = new Configuration();
Configuration.Resource resource = new Configuration.Resource(sourceFile);
conff.addResource(resource);
return conff;
}
/**
* build data model
*
* @param conf
* @return
* @throws LibrecException
*/
protected DataModel dataModel(Configuration conf) throws LibrecException {
DataModel dataModel = new TextDataModel(conf);
dataModel.buildDataModel();
return dataModel;
}
/**
* @param configFilePath
* @param dataModel
* @return
* @throws Exception
*/
protected Recommender recommender(String configFilePath, DataModel dataModel) throws Exception {
// configuration
Configuration conf = this.configInfo(configFilePath);
// context
RecommenderContext context = this.context(conf, dataModel);
// training
String algClass = conf.get("rec.recommender.fullClassName");
Recommender newRecommender = this.newRecommemder(context, algClass);
return newRecommender;
}
/**
* Set recommendation context
*
* @param conf
* @param dataModel
* @return
* @throws LibrecException
*/
protected RecommenderContext context(Configuration conf, DataModel dataModel) throws LibrecException {
RecommenderContext context = new RecommenderContext(conf, dataModel);
RecommenderSimilarity similarity = new PCCSimilarity();
similarity.buildSimilarityMatrix(dataModel);
context.setSimilarity(similarity);
return context;
}
/**
* @param context
* @param algClass
* @return
* @throws Exception
*/
protected Recommender newRecommemder(RecommenderContext context, String algClass) throws Exception {
Object newAlg = Class.forName(algClass).newInstance();
Recommender recommender = (Recommender) newAlg;
recommender.setContext(context);
recommender.train(context);
return recommender;
}
/**
*
* @param recommender
* @return
* @throws LibrecException
*/
// protected double evaluation(Recommender recommender) throws LibrecException {
// RecommenderEvaluator evaluator = new RMSEEvaluator();
// double evaluateValue = recommender.evaluate(evaluator);
// System.out.println("RMSE:"+ recommender.evaluate(evaluator));
// return evaluateValue;
// }
/**
*
* @param recommender
* @return
* @throws LibrecException
*/
// protected List filterRecommandResult(Recommender recommender) throws LibrecException {
// List recommendedItemList = recommender.getRecommendedList();
// RecommendedFilter filter = new GenericRecommendedFilter();
// recommendedItemList = filter.filter(recommendedItemList);
// return recommendedItemList;
// }
/**
* @return
*/
protected List recommendedResult() {
return this.recommendedItemFinal;
}
/**
* @return
*/
protected int getTotalItems() {
return recommendedItemListOfAlgs.size();
}
/**
* @return
*/
protected int getNumsOfAlg() {
return this.numsOfAlg;
}
/**
* @param dataModel
*/
protected void trainDataRealRating(DataModel dataModel) {
//Get SparseMatrix
SequentialAccessSparseMatrix trainMatrix = (SequentialAccessSparseMatrix) dataModel.getTrainDataSet();
Iterator trainMatrixIter = trainMatrix.iterator();
while (trainMatrixIter.hasNext()) {
MatrixEntry trainMatrixEntry = trainMatrixIter.next();
double trainRealRating = trainMatrixEntry.get();
this.realRating.add(trainRealRating);
}
}
/**
* Save result.
*
* @param recommendedList list of recommended items
* @throws LibrecException if error occurs
* @throws IOException if I/O error occurs
* @throws ClassNotFoundException if class not found error occurs
*/
public void saveResult(List recommendedList, Configuration confAlg, int tempNums, String resultType) throws LibrecException, IOException, ClassNotFoundException {
if (recommendedList != null && recommendedList.size() > 0) {
String algoSimpleName = DriverClassUtil.getDriverName(recommenderClass(confAlg));
String outputPath = this.conf.get("dfs.result.dir") + "/ensemble/" + algoSimpleName + "-" + tempNums + "/" + resultType;
if (null != dataModel && (dataModel.getDataSplitter() instanceof KCVDataSplitter || dataModel.getDataSplitter() instanceof LOOCVDataSplitter) && null != conf.getInt("data.splitter.cv.index")) {
outputPath = outputPath + "/fold-" + String.valueOf(this.conf.getInt("data.splitter.cv.index"));
}
LOG.info("Result path is " + outputPath);
// Convert itemList to string
StringBuilder sb = new StringBuilder();
for (RecommendedItem recItem : recommendedList) {
String userId = recItem.getUserId();
String itemId = recItem.getItemId();
String value = String.valueOf(recItem.getValue());
sb.append(userId).append(",").append(itemId).append(",").append(value).append("\n");
}
String resultData = sb.toString();
// save resultData
try {
FileUtil.writeString(outputPath, resultData);
} catch (Exception e) {
e.printStackTrace();
}
}
}
/**
* Get recommender class. {@code Recommender}.
*
* @return recommender class object
* @throws ClassNotFoundException if can't find the class of recommender
* @throws IOException If an I/O error occurs.
*/
@SuppressWarnings("unchecked")
public Class recommenderClass(Configuration confAlg) throws ClassNotFoundException, IOException {
return (Class) DriverClassUtil.getClass(confAlg.get("rec.recommender.class"));
}
/**
* @return
*/
protected List ensembelParameter() {
return Collections.emptyList();
}
/**
*
* @param savePath
* @return
*/
// protected Boolean saveRecommendResult(String savePath){
// return Boolean.TRUE;
// }
}