MultithreadedBatchItemSimilaritiesTest.java example

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 * this work for additional information regarding copyright ownership.
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package org.apache.mahout.cf.taste.impl.similarity.precompute;

import java.io.IOException;
import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.model.GenericDataModel;
import org.apache.mahout.cf.taste.impl.model.GenericPreference;
import org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray;
import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender;
import org.apache.mahout.cf.taste.similarity.precompute.BatchItemSimilarities;
import org.apache.mahout.cf.taste.similarity.precompute.SimilarItemsWriter;
import org.junit.Test;

import java.util.Arrays;

import static org.junit.Assert.assertEquals;
import static org.junit.Assert.fail;
import static org.mockito.Mockito.mock;

public class MultithreadedBatchItemSimilaritiesTest {

  @Test
  public void lessItemsThanBatchSize() throws Exception {

    FastByIDMap<PreferenceArray> userData = new FastByIDMap<>();
    userData.put(1, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1, 1, 1),
        new GenericPreference(1, 2, 1), new GenericPreference(1, 3, 1))));
    userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
        new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));

    DataModel dataModel = new GenericDataModel(userData);
    ItemBasedRecommender recommender =
        new GenericItemBasedRecommender(dataModel, new TanimotoCoefficientSimilarity(dataModel));

    BatchItemSimilarities batchSimilarities = new MultithreadedBatchItemSimilarities(recommender, 10);

    batchSimilarities.computeItemSimilarities(1, 1, mock(SimilarItemsWriter.class));
  }

  @Test(expected = IOException.class)
  public void higherDegreeOfParallelismThanBatches() throws Exception {

    FastByIDMap<PreferenceArray> userData = new FastByIDMap<>();
    userData.put(1, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1, 1, 1),
        new GenericPreference(1, 2, 1), new GenericPreference(1, 3, 1))));
    userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
        new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));

    DataModel dataModel = new GenericDataModel(userData);
    ItemBasedRecommender recommender =
        new GenericItemBasedRecommender(dataModel, new TanimotoCoefficientSimilarity(dataModel));

    BatchItemSimilarities batchSimilarities = new MultithreadedBatchItemSimilarities(recommender, 10);

    // Batch size is 100, so we only get 1 batch from 3 items, but we use a degreeOfParallelism of 2
    batchSimilarities.computeItemSimilarities(2, 1, mock(SimilarItemsWriter.class));
    fail();
  }

  @Test
  public void testCorrectNumberOfOutputSimilarities() throws Exception {
    FastByIDMap<PreferenceArray> userData = new FastByIDMap<>();
    userData.put(1, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1, 1, 1),
        new GenericPreference(1, 2, 1), new GenericPreference(1, 3, 1))));
    userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
        new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));

    DataModel dataModel = new GenericDataModel(userData);
    ItemBasedRecommender recommender =
        new GenericItemBasedRecommender(dataModel, new TanimotoCoefficientSimilarity(dataModel));

    BatchItemSimilarities batchSimilarities = new MultithreadedBatchItemSimilarities(recommender, 10, 2);

    int numOutputSimilarities = batchSimilarities.computeItemSimilarities(2, 1, mock(SimilarItemsWriter.class));
    assertEquals(numOutputSimilarities, 10);
  }

}