/**
* @param dataFile file containing preferences data
* @throws FileNotFoundException if dataFile does not exist
*/
public FileDataModel(String dataFile, bool autoReload)
{
if (String.IsNullOrEmpty(dataFile))
{
throw new ArgumentNullException("dataFile is null");
}
if (File.Exists(dataFile))
{
throw new FileNotFoundException(dataFile);
}
if (log.IsInfoEnabled)
{
log.Info("Creating FileDataModel for file " + dataFile);
}
this.dataFile = dataFile;
this.lastModified = File.GetLastWriteTime(dataFile);
this.refreshLock = new ReentrantLock();
this.reloadLock = new ReentrantLock();
this.useReload = autoReload;
if (autoReload)
{
// Create the delegate that invokes methods for the timer.
TimerCallback timerDelegate =
new TimerCallback(CheckStatus);
// Schedule next refresh
timer = new Timer(timerDelegate, this, RELOAD_CHECK_INTERVAL_MS, RELOAD_CHECK_INTERVAL_MS);
}
}
protected AbstractRecommender(DataModel dataModel)
{
if (dataModel == null)
{
throw new ArgumentNullException("dataModel is null");
}
this.dataModel = dataModel;
this.refreshLock = new ReentrantLock();
}
public SpearmanCorrelation(UserCorrelation rankingUserCorrelation)
{
if (rankingUserCorrelation == null)
{
throw new ArgumentNullException("rankingUserCorrelation is null");
}
this.rankingUserCorrelation = rankingUserCorrelation;
this.refreshLock = new ReentrantLock();
}
public SpearmanCorrelation(DataModel dataModel)
{
if (dataModel == null)
{
throw new ArgumentNullException("dataModel is null");
}
this.rankingUserCorrelation = new PearsonCorrelation(dataModel);
this.refreshLock = new ReentrantLock();
}
/// <summary>
/// <p>Creates a new {@link MemoryDiffStorage}.</p>
/// <p>See {@link taste.Recommender.SlopeOne.SlopeOneRecommender} for the
/// meaning of <code>stdDevWeighted</code>. If <code>compactAverages</code>
/// is set, this uses alternate data structures ({@link CompactRunningAverage} versus
/// {@link FullRunningAverage}) that use almost 50% less memory but store item-item
/// averages less accurately. <code>maxEntries</code> controls the maximum number of item-item average
/// preference differences that will be tracked internally. After the limit is reached,
/// if a new item-item pair is observed in the data it will be ignored. This is recommended for large datasets.
/// The first <code>maxEntries</code> item-item pairs observed in the data are tracked. Assuming that item
/// ratings are reasonably distributed among users, this should only ignore item-item pairs that are very
/// infrequently co-rated by a user.</p>
/// <p> The intuition is that data on these infrequently co-rated item-item pairs is less reliable and should
/// be the first that is ignored. This parameter can be used to limit the memory requirements of
/// {@link SlopeOneRecommender}, which otherwise grow as the square of the number of items that exist
/// in the {@link DataModel}. Memory requirements can reach gigabytes with only about 10000 items, so this may
/// be necessary on larger datasets.</p>
/// </summary>
/// <param name="dataModel"></param>
/// <param name="stdDevWeighted">see {@link taste.Recommender.SlopeOne.SlopeOneRecommender}</param>
/// <param name="compactAverages">
/// if <code>true</code>, use {@link CompactRunningAverage} instead of {@link FullRunningAverage} internally
/// </param>
/// <param name="maxEntries">maximum number of item-item average preference differences to track internally</param>
public MemoryDiffStorage(DataModel dataModel,
bool stdDevWeighted,
bool compactAverages,
long maxEntries)
{
if (dataModel == null)
{
throw new ArgumentNullException("dataModel is null");
}
if (maxEntries <= 0L)
{
throw new ArgumentException("maxEntries must be positive");
}
this.dataModel = dataModel;
this.stdDevWeighted = stdDevWeighted;
this.compactAverages = compactAverages;
this.maxEntries = maxEntries;
this.averageDiffs = new Dictionary<Object, Dictionary<Object, RunningAverage>>(1003);
this.averageItemPref = new Dictionary<Object, RunningAverage>(101);
this.buildAverageDiffsLock = new ReaderWriterLock();
this.refreshLock = new ReentrantLock();
BuildAverageDiffs();
}
protected AbstractADODiffStorage(AbstractADODataModel dataModel,
String getDiffSQL,
String getDiffsSQL,
String getAverageItemPrefSQL,
String[] updateDiffSQLs,
String[] removeDiffSQLs,
String getRecommendableItemsSQL,
String deleteDiffsSQL,
String createDiffsSQL,
String diffsExistSQL,
int minDiffCount)
{
if (dataModel == null)
{
throw new ArgumentNullException("dataModel is null");
}
if (minDiffCount < 0)
{
throw new ArgumentException("minDiffCount is not positive");
}
this.dataModel = dataModel;
this.getDiffSQL = getDiffSQL;
this.getDiffsSQL = getDiffsSQL;
this.getAverageItemPrefSQL = getAverageItemPrefSQL;
this.updateDiffSQLs = updateDiffSQLs;
this.removeDiffSQLs = removeDiffSQLs;
this.getRecommendableItemsSQL = getRecommendableItemsSQL;
this.deleteDiffsSQL = deleteDiffsSQL;
this.createDiffsSQL = createDiffsSQL;
this.diffsExistSQL = diffsExistSQL;
this.minDiffCount = minDiffCount;
this.refreshLock = new ReentrantLock();
if (IsDiffsExist())
{
log.Info("Diffs already exist in database; using them instead of recomputing");
}
else
{
log.Info("No diffs exist in database; recomputing...");
BuildAverageDiffs();
}
}