Package org.apache.mahout.math.als

Examples of org.apache.mahout.math.als.ImplicitFeedbackAlternatingLeastSquaresSolver

271
272
273
274
275
276
277
278
279
      int numFeatures = ctx.getConfiguration().getInt(NUM_FEATURES, -1);

      Path YPath = new Path(ctx.getConfiguration().get(FEATURE_MATRIX));
      OpenIntObjectHashMap<Vector> Y = ALSUtils.readMatrixByRows(YPath, ctx.getConfiguration());

      solver = new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, Y);

      Preconditions.checkArgument(numFeatures > 0, "numFeatures was not set correctly!");
    }
View Full Code Here
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
      /* fix M - compute U */
      ExecutorService queue = createQueue();
      LongPrimitiveIterator userIDsIterator = dataModel.getUserIDs();
      try {

        final ImplicitFeedbackAlternatingLeastSquaresSolver implicitFeedbackSolver = usesImplicitFeedback
            ? new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, itemY) : null;

        while (userIDsIterator.hasNext()) {
          final long userID = userIDsIterator.nextLong();
          final LongPrimitiveIterator itemIDsFromUser = dataModel.getItemIDsFromUser(userID).iterator();
          final PreferenceArray userPrefs = dataModel.getPreferencesFromUser(userID);
          queue.execute(new Runnable() {
            @Override
            public void run() {
              List<Vector> featureVectors = Lists.newArrayList();
              while (itemIDsFromUser.hasNext()) {
                long itemID = itemIDsFromUser.nextLong();
                featureVectors.add(features.getItemFeatureColumn(itemIndex(itemID)));
              }

              Vector userFeatures = usesImplicitFeedback
                  ? implicitFeedbackSolver.solve(sparseUserRatingVector(userPrefs))
                  : AlternatingLeastSquaresSolver.solve(featureVectors, ratingVector(userPrefs), lambda, numFeatures);

              features.setFeatureColumnInU(userIndex(userID), userFeatures);
            }
          });
        }
      } finally {
        queue.shutdown();
        try {
          queue.awaitTermination(dataModel.getNumUsers(), TimeUnit.SECONDS);
        } catch (InterruptedException e) {
          log.warn("Error when computing user features", e);
        }
      }

      /* fix U - compute M */
      queue = createQueue();
      LongPrimitiveIterator itemIDsIterator = dataModel.getItemIDs();
      try {

        final ImplicitFeedbackAlternatingLeastSquaresSolver implicitFeedbackSolver = usesImplicitFeedback
            ? new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, userY) : null;

        while (itemIDsIterator.hasNext()) {
          final long itemID = itemIDsIterator.nextLong();
          final PreferenceArray itemPrefs = dataModel.getPreferencesForItem(itemID);
          queue.execute(new Runnable() {
            @Override
            public void run() {
              List<Vector> featureVectors = Lists.newArrayList();
              for (Preference pref : itemPrefs) {
                long userID = pref.getUserID();
                featureVectors.add(features.getUserFeatureColumn(userIndex(userID)));
              }

              Vector itemFeatures = usesImplicitFeedback
                  ? implicitFeedbackSolver.solve(sparseItemRatingVector(itemPrefs))
                  : AlternatingLeastSquaresSolver.solve(featureVectors, ratingVector(itemPrefs), lambda, numFeatures);

              features.setFeatureColumnInM(itemIndex(itemID), itemFeatures);
            }
          });
View Full Code Here
41
42
43
44
45
46
47
48
    int numFeatures = conf.getInt(ParallelALSFactorizationJob.NUM_FEATURES, -1);
    int numEntities = Integer.parseInt(conf.get(ParallelALSFactorizationJob.NUM_ENTITIES));

    Preconditions.checkArgument(numFeatures > 0, "numFeatures was not set correctly!");

    return new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha,
        ALS.readMatrixByRowsFromDistributedCache(numEntities, conf));
  }
View Full Code Here
48
49
50
51
52
53
54
55
56
  }

  @Override
  protected void map(IntWritable userOrItemID, VectorWritable ratingsWritable, Context ctx)
    throws IOException, InterruptedException {
    ImplicitFeedbackAlternatingLeastSquaresSolver solver = getSharedInstance();
    uiOrmj.set(solver.solve(ratingsWritable.get()));
    ctx.write(userOrItemID, uiOrmj);
  }
View Full Code Here
41
42
43
44
45
46
47
48
    int numFeatures = conf.getInt(ParallelALSFactorizationJob.NUM_FEATURES, -1);
    int numEntities = Integer.parseInt(conf.get(ParallelALSFactorizationJob.NUM_ENTITIES));

    Preconditions.checkArgument(numFeatures > 0, "numFeatures must be greater then 0!");

    return new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha,
        ALS.readMatrixByRowsFromDistributedCache(numEntities, conf));
  }
View Full Code Here
48
49
50
51
52
53
54
55
56
  }

  @Override
  protected void map(IntWritable userOrItemID, VectorWritable ratingsWritable, Context ctx)
    throws IOException, InterruptedException {
    ImplicitFeedbackAlternatingLeastSquaresSolver solver = getSharedInstance();
    uiOrmj.set(solver.solve(ratingsWritable.get()));
    ctx.write(userOrItemID, uiOrmj);
  }
View Full Code Here
279
280
281
282
283
284
285
286
287
      int numFeatures = ctx.getConfiguration().getInt(NUM_FEATURES, -1);

      Path YPath = new Path(ctx.getConfiguration().get(FEATURE_MATRIX));
      OpenIntObjectHashMap<Vector> Y = ALSUtils.readMatrixByRows(YPath, ctx.getConfiguration());

      solver = new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, Y);

      Preconditions.checkArgument(numFeatures > 0, "numFeatures was not set correctly!");
    }
View Full Code Here
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
      /* fix M - compute U */
      ExecutorService queue = createQueue();
      LongPrimitiveIterator userIDsIterator = dataModel.getUserIDs();
      try {

        final ImplicitFeedbackAlternatingLeastSquaresSolver implicitFeedbackSolver = usesImplicitFeedback
            ? new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, itemY, numTrainingThreads)
            : null;

        while (userIDsIterator.hasNext()) {
          final long userID = userIDsIterator.nextLong();
          final LongPrimitiveIterator itemIDsFromUser = dataModel.getItemIDsFromUser(userID).iterator();
          final PreferenceArray userPrefs = dataModel.getPreferencesFromUser(userID);
          queue.execute(new Runnable() {
            @Override
            public void run() {
              List<Vector> featureVectors = Lists.newArrayList();
              while (itemIDsFromUser.hasNext()) {
                long itemID = itemIDsFromUser.nextLong();
                featureVectors.add(features.getItemFeatureColumn(itemIndex(itemID)));
              }

              Vector userFeatures = usesImplicitFeedback
                  ? implicitFeedbackSolver.solve(sparseUserRatingVector(userPrefs))
                  : AlternatingLeastSquaresSolver.solve(featureVectors, ratingVector(userPrefs), lambda, numFeatures);

              features.setFeatureColumnInU(userIndex(userID), userFeatures);
            }
          });
        }
      } finally {
        queue.shutdown();
        try {
          queue.awaitTermination(dataModel.getNumUsers(), TimeUnit.SECONDS);
        } catch (InterruptedException e) {
          log.warn("Error when computing user features", e);
        }
      }

      /* fix U - compute M */
      queue = createQueue();
      LongPrimitiveIterator itemIDsIterator = dataModel.getItemIDs();
      try {

        final ImplicitFeedbackAlternatingLeastSquaresSolver implicitFeedbackSolver = usesImplicitFeedback
            ? new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, userY, numTrainingThreads)
            : null;

        while (itemIDsIterator.hasNext()) {
          final long itemID = itemIDsIterator.nextLong();
          final PreferenceArray itemPrefs = dataModel.getPreferencesForItem(itemID);
          queue.execute(new Runnable() {
            @Override
            public void run() {
              List<Vector> featureVectors = Lists.newArrayList();
              for (Preference pref : itemPrefs) {
                long userID = pref.getUserID();
                featureVectors.add(features.getUserFeatureColumn(userIndex(userID)));
              }

              Vector itemFeatures = usesImplicitFeedback
                  ? implicitFeedbackSolver.solve(sparseItemRatingVector(itemPrefs))
                  : AlternatingLeastSquaresSolver.solve(featureVectors, ratingVector(itemPrefs), lambda, numFeatures);

              features.setFeatureColumnInM(itemIndex(itemID), itemFeatures);
            }
          });
View Full Code Here
41
42
43
44
45
46
47
48
    int numFeatures = conf.getInt(ParallelALSFactorizationJob.NUM_FEATURES, -1);
    int numEntities = Integer.parseInt(conf.get(ParallelALSFactorizationJob.NUM_ENTITIES));

    Preconditions.checkArgument(numFeatures > 0, "numFeatures must be greater then 0!");

    return new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha,
        ALS.readMatrixByRowsFromDistributedCache(numEntities, conf), 1);
  }
View Full Code Here
48
49
50
51
52
53
54
55
56
  }

  @Override
  protected void map(IntWritable userOrItemID, VectorWritable ratingsWritable, Context ctx)
    throws IOException, InterruptedException {
    ImplicitFeedbackAlternatingLeastSquaresSolver solver = getSharedInstance();
    uiOrmj.set(solver.solve(ratingsWritable.get()));
    ctx.write(userOrItemID, uiOrmj);
  }
View Full Code Here
TOP

Related Classes of org.apache.mahout.math.als.ImplicitFeedbackAlternatingLeastSquaresSolver

  • org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJob$SolveImplicitFeedbackMapper
  • org.apache.mahout.cf.taste.hadoop.als.SolveImplicitFeedbackMapper
  • org.apache.mahout.cf.taste.impl.recommender.svd.ALSWRFactorizer
  • org.apache.mahout.math.DenseMatrix
  • org.apache.mahout.math.DenseVector
  • org.apache.mahout.math.list.IntArrayList
  • org.apache.mahout.math.Matrix
  • org.apache.mahout.math.QRDecomposition
  • org.apache.mahout.math.Vector
  • java.util.concurrent.ExecutorService
    • 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.