/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
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package org.apache.mahout.cf.taste.impl.similarity.precompute;
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.io.IOException;
import java.util.Arrays;
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<PreferenceArray>();
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
public void higherDegreeOfParallelismThanBatches() throws Exception {
FastByIDMap<PreferenceArray> userData = new FastByIDMap<PreferenceArray>();
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);
try {
// 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();
} catch (IOException e) {}
}
}