Package org.apache.mahout.cf.taste.impl.common

Examples of org.apache.mahout.cf.taste.impl.common.FastIDSet

211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
    }
  }
 
  @Override
  public FastIDSet getRecommendableItemIDs(long userID) throws TasteException {
    FastIDSet result;
    try {
      buildAverageDiffsLock.readLock().lock();
      result = allRecommendableItemIDs.clone();
    } finally {
      buildAverageDiffsLock.readLock().unlock();
    }
    Iterator<Long> it = result.iterator();
    while (it.hasNext()) {
      if (dataModel.getPreferenceValue(userID, it.next()) != null) {
        it.remove();
      }
    }
View Full Code Here
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
    }
    averageDiffs.rehash();
  }
 
  private void updateAllRecommendableItems() throws TasteException {
    FastIDSet ids = new FastIDSet(dataModel.getNumItems());
    for (Map.Entry<Long,FastByIDMap<RunningAverage>> entry : averageDiffs.entrySet()) {
      ids.add(entry.getKey());
      LongPrimitiveIterator it = entry.getValue().keySetIterator();
      while (it.hasNext()) {
        ids.add(it.next());
      }
    }
    allRecommendableItemIDs.clear();
    allRecommendableItemIDs.addAll(ids);
    allRecommendableItemIDs.rehash();
View Full Code Here
61
62
63
64
65
66
67
68
69
70
71
72
  }
 
  @Override
  protected FastIDSet getAllOtherItems(long[] theNeighborhood, long theUserID) throws TasteException {
    DataModel dataModel = getDataModel();
    FastIDSet possibleItemIDs = new FastIDSet();
    for (long userID : theNeighborhood) {
      possibleItemIDs.addAll(dataModel.getItemIDsFromUser(userID));
    }
    possibleItemIDs.removeAll(dataModel.getItemIDsFromUser(theUserID));
    return possibleItemIDs;
  }
View Full Code Here
86
87
88
89
90
91
92
93
94
95
    log.info("Creating FileDataModel for file {}", dataFile);
    this.dataFile = dataFile.getAbsoluteFile();
    this.lastModified = dataFile.lastModified();
    this.maxEntries = maxEntries;
    this.averageDiffs = new FastByIDMap<FastByIDMap<RunningAverage>>();
    this.allRecommendableItemIDs = new FastIDSet();
    this.buildAverageDiffsLock = new ReentrantReadWriteLock();

    buildDiffs();
  }
View Full Code Here
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
    if (theNeighborhood.length == 0) {
      return Collections.emptyList();
    }

    FastIDSet allItemIDs = getAllOtherItems(theNeighborhood, userID);

    TopItems.Estimator<Long> estimator = new Estimator(userID, theNeighborhood);

    List<RecommendedItem> topItems = TopItems
        .getTopItems(howMany, allItemIDs.iterator(), rescorer, estimator);

    log.debug("Recommendations are: {}", topItems);
    return topItems;
  }
View Full Code Here
158
159
160
161
162
163
164
165
166
167
168
169
    return estimate;
  }
 
  protected FastIDSet getAllOtherItems(long[] theNeighborhood, long theUserID) throws TasteException {
    DataModel dataModel = getDataModel();
    FastIDSet possibleItemIDs = new FastIDSet();
    for (long userID : theNeighborhood) {
      possibleItemIDs.addAll(dataModel.getItemIDsFromUser(userID));
    }
    possibleItemIDs.removeAll(dataModel.getItemIDsFromUser(theUserID));
    return possibleItemIDs;
  }
View Full Code Here
28
29
30
31
32
33
34
35
36
37
38
39
40
*/
public final class AllUnknownItemsCandidateItemsStrategy implements CandidateItemsStrategy {

  @Override
  public FastIDSet getCandidateItems(long userID, DataModel dataModel) throws TasteException {
    FastIDSet possibleItemIDs = new FastIDSet(dataModel.getNumItems());
    LongPrimitiveIterator allItemIDs = dataModel.getItemIDs();
    while (allItemIDs.hasNext()) {
      possibleItemIDs.add(allItemIDs.nextLong());
    }
    possibleItemIDs.removeAll(dataModel.getItemIDsFromUser(userID));
    return possibleItemIDs;
  }
View Full Code Here
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
*/
public final class PreferredItemsNeighborhoodCandidateItemsStrategyTest extends TasteTestCase {

  @Test
  public void testStrategy() throws TasteException {
    FastIDSet itemIDsFromUser123 = new FastIDSet();
    itemIDsFromUser123.add(1L);

    FastIDSet itemIDsFromUser456 = new FastIDSet();
    itemIDsFromUser456.add(1L);
    itemIDsFromUser456.add(2L);

    List<Preference> prefs = new ArrayList<Preference>();
    prefs.add(new GenericPreference(123L, 1L, 1.0f));
    prefs.add(new GenericPreference(456L, 1L, 1.0f));
    PreferenceArray preferencesForItem1 = new GenericItemPreferenceArray(prefs);

    DataModel dataModel = EasyMock.createMock(DataModel.class);
    EasyMock.expect(dataModel.getItemIDsFromUser(123L)).andReturn(itemIDsFromUser123);
    EasyMock.expect(dataModel.getPreferencesForItem(1L)).andReturn(preferencesForItem1);
    EasyMock.expect(dataModel.getItemIDsFromUser(123L)).andReturn(itemIDsFromUser123);
    EasyMock.expect(dataModel.getItemIDsFromUser(456L)).andReturn(itemIDsFromUser456);

    CandidateItemsStrategy strategy = new PreferredItemsNeighborhoodCandidateItemsStrategy();

    EasyMock.replay(dataModel);

    FastIDSet candidateItems = strategy.getCandidateItems(123L, dataModel);
    assertEquals(1, candidateItems.size());
    assertTrue(candidateItems.contains(2L));

    EasyMock.verify(dataModel);
  }
View Full Code Here
84
85
86
87
88
89
90
  private PreferenceArray tempPrefs;
  private final FastIDSet prefItemIDs;
 
  public PlusAnonymousUserDataModel(DataModel delegate) {
    this.delegate = delegate;
    this.prefItemIDs = new FastIDSet();
  }
View Full Code Here
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
  protected float doEstimatePreference(long theUserID, long itemID) throws TasteException {
   
    DataModel dataModel = getDataModel();
    PreferenceArray prefs = dataModel.getPreferencesFromUser(theUserID);
    int size = prefs.length();
    FastIDSet possibleItemIDs = new FastIDSet(size);
    for (int i = 0; i < size; i++) {
      possibleItemIDs.add(prefs.getItemID(i));
    }
    possibleItemIDs.remove(itemID);
   
    List<RecommendedItem> mostSimilar = mostSimilarItems(itemID, possibleItemIDs.iterator(),
      neighborhoodSize, null);
    long[] theNeighborhood = new long[mostSimilar.size()];
    int nOffset = 0;
    for (RecommendedItem rec : mostSimilar) {
      theNeighborhood[nOffset++] = rec.getItemID();
View Full Code Here
TOP

Related Classes of org.apache.mahout.cf.taste.impl.common.FastIDSet

  • io.prediction.engines.base.mahout.AllValidItemsCandidateItemsStrategy
  • mia.recommender.ch05.GenderRescorer
  • mia.recommender.ch05.LibimsetiRecommender
  • org.apache.mahout.cf.taste.example.kddcup.track1.svd.DataModelFactorizablePreferences
  • org.apache.mahout.cf.taste.example.kddcup.track2.TrackData
  • org.apache.mahout.cf.taste.example.kddcup.track2.TrackItemSimilarity
  • org.apache.mahout.cf.taste.hadoop.item.AggregateAndRecommendReducer
  • org.apache.mahout.cf.taste.hadoop.item.IDReader
  • org.apache.mahout.cf.taste.hadoop.item.IDReaderTest
  • org.apache.mahout.cf.taste.hadoop.item.RecommenderJobTest
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.