/**
* 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.
*/
package org.apache.mahout.cf.taste.impl.recommender.slopeone;
import org.apache.mahout.cf.taste.common.NoSuchUserException;
import org.apache.mahout.cf.taste.common.Weighting;
import org.apache.mahout.cf.taste.impl.TasteTestCase;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.common.RunningAverage;
import org.apache.mahout.cf.taste.impl.common.RunningAverageAndStdDev;
import org.apache.mahout.cf.taste.model.DataModel;
import org.junit.Test;
/** Tests {@link MemoryDiffStorage}. */
@Deprecated
public final class MemoryDiffStorageTest extends TasteTestCase {
@Test
public void testRecommendableIDsVariedWeighted() throws Exception {
DataModel model = getDataModelVaried();
MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.WEIGHTED, Long.MAX_VALUE);
FastIDSet recommendableItemIDs = storage.getRecommendableItemIDs(1);
assertEquals(3, recommendableItemIDs.size());
assertTrue(recommendableItemIDs.contains(1));
recommendableItemIDs = storage.getRecommendableItemIDs(2);
assertEquals(2, recommendableItemIDs.size());
assertTrue(recommendableItemIDs.contains(2));
assertTrue(recommendableItemIDs.contains(3));
recommendableItemIDs = storage.getRecommendableItemIDs(3);
assertEquals(1, recommendableItemIDs.size());
assertTrue(recommendableItemIDs.contains(3));
recommendableItemIDs = storage.getRecommendableItemIDs(4);
assertEquals(0, recommendableItemIDs.size());
// the last item has only one recommendation, and so only 4 items are usable
recommendableItemIDs = storage.getRecommendableItemIDs(5);
assertEquals(0, recommendableItemIDs.size());
}
@Test
public void testRecommendableIDsPockedUnweighted() throws Exception {
DataModel model = getDataModelPocked();
MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, Long.MAX_VALUE);
FastIDSet recommendableItemIDs = storage.getRecommendableItemIDs(1);
assertEquals(0, recommendableItemIDs.size());
recommendableItemIDs = storage.getRecommendableItemIDs(2);
assertEquals(1, recommendableItemIDs.size());
recommendableItemIDs = storage.getRecommendableItemIDs(3);
assertEquals(0, recommendableItemIDs.size());
recommendableItemIDs = storage.getRecommendableItemIDs(4);
assertEquals(0, recommendableItemIDs.size());
}
@Test (expected=NoSuchUserException.class)
public void testUnRecommendableID() throws Exception {
DataModel model = getDataModel();
MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.WEIGHTED, Long.MAX_VALUE);
storage.getRecommendableItemIDs(0);
}
@Test
public void testGetDiff() throws Exception {
DataModel model = getDataModel();
MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, Long.MAX_VALUE);
RunningAverage average = storage.getDiff(1, 2);
assertEquals(0.23333333333333334, average.getAverage(), EPSILON);
assertEquals(3, average.getCount());
}
@Test
public void testAdd() throws Exception {
DataModel model = getDataModel();
MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, Long.MAX_VALUE);
RunningAverage average1 = storage.getDiff(0, 2);
assertEquals(0.1, average1.getAverage(), EPSILON);
assertEquals(3, average1.getCount());
RunningAverage average2 = storage.getDiff(1, 2);
assertEquals(0.23333332935969034, average2.getAverage(), EPSILON);
assertEquals(3, average2.getCount());
storage.addItemPref(1, 2, 0.8f);
average1 = storage.getDiff(0, 2);
assertEquals(0.25, average1.getAverage(), EPSILON);
assertEquals(4, average1.getCount());
average2 = storage.getDiff(1, 2);
assertEquals(0.3, average2.getAverage(), EPSILON);
assertEquals(4, average2.getCount());
}
@Test
public void testUpdate() throws Exception {
DataModel model = getDataModel();
MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, Long.MAX_VALUE);
RunningAverage average = storage.getDiff(1, 2);
assertEquals(0.23333332935969034, average.getAverage(), EPSILON);
assertEquals(3, average.getCount());
storage.updateItemPref(1, 0.5f);
average = storage.getDiff(1, 2);
assertEquals(0.06666666666666668, average.getAverage(), EPSILON);
assertEquals(3, average.getCount());
}
@Test
public void testRemove() throws Exception {
DataModel model = getDataModel();
MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, Long.MAX_VALUE);
RunningAverage average1 = storage.getDiff(0, 2);
assertEquals(0.1, average1.getAverage(), EPSILON);
assertEquals(3, average1.getCount());
RunningAverage average2 = storage.getDiff(1, 2);
assertEquals(0.23333332935969034, average2.getAverage(), EPSILON);
assertEquals(3, average2.getCount());
storage.removeItemPref(4, 2, 0.8f);
average1 = storage.getDiff(0, 2);
assertEquals(0.1, average1.getAverage(), EPSILON);
assertEquals(2, average1.getCount());
average2 = storage.getDiff(1, 2);
assertEquals(0.1, average2.getAverage(), EPSILON);
assertEquals(2, average2.getCount());
}
@Test (expected=UnsupportedOperationException.class)
public void testUpdateWeighted() throws Exception {
DataModel model = getDataModelVaried();
MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.WEIGHTED, Long.MAX_VALUE);
storage.updateItemPref(2, 0.8f);
}
@Test
public void testRemovePref() throws Exception {
double eps = 0.0001;
DataModel model = getDataModelPocked();
MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.WEIGHTED, Long.MAX_VALUE);
RunningAverageAndStdDev average = (RunningAverageAndStdDev) storage.getDiff(0, 1);
assertEquals(-0.033333, average.getAverage(), eps);
assertEquals(0.32145, average.getStandardDeviation(), eps);
assertEquals(3, average.getCount());
storage.removeItemPref(2, 1, 0.1f);
average = (RunningAverageAndStdDev) storage.getDiff(0, 1);
assertEquals(0.00000001, average.getAverage(), eps);
assertEquals(0.44721, average.getStandardDeviation(), eps);
assertEquals(2, average.getCount());
}
static DataModel getDataModelVaried() {
return getDataModel(
new long[] {1, 2, 3, 4, 5},
new Double[][] {
{0.2},
{0.4, 0.5},
{0.7, 0.1, 0.5},
{0.7, 0.3, 0.8, 0.1},
{0.2, 0.3, 0.6, 0.1, 0.3},
});
}
static DataModel getDataModelPocked() {
return getDataModel(
new long[] {1, 2, 3, 4},
new Double[][] {
{0.1, 0.3},
{0.2},
{0.4, 0.5},
{0.7, 0.3, 0.8},
});
}
static DataModel getDataModelLarge() {
return getDataModel(
new long[] {1, 2, 3, 4, 5, 6, 7},
new Double[][] {
{0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2},
{0.4, 0.5, 0.3, 0.3, 0.3, 0.3, 0.3},
{0.7, 0.1, 0.5, 0.2, 0.7, 0.8, 0.9},
{0.7, 0.3, 0.8, 0.1, 0.6, 0.6, 0.6},
{0.2, 0.3, 0.6, 0.1, 0.3, 0.4, 0.4},
{0.2, 0.3, 0.6, 0.1, 0.3, 0.4, 0.4},
{0.2, 0.3, 0.6, 0.1, 0.3, 0.5, 0.5},
});
}
}