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* Licensed to the Apache Software Foundation (ASF) under one or more
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* 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
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*
* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
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package org.apache.mahout.math.neighborhood;
import java.util.Arrays;
import java.util.List;
import com.google.common.collect.Iterables;
import com.google.common.collect.Lists;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.MatrixSlice;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.jet.math.Constants;
import org.apache.mahout.math.random.MultiNormal;
import org.apache.mahout.math.random.WeightedThing;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.junit.runners.Parameterized;
@RunWith(Parameterized.class)
public class SearchSanityTest extends MahoutTestCase {
private static final int NUM_DATA_POINTS = 1 << 13;
private static final int NUM_DIMENSIONS = 20;
private static final int NUM_PROJECTIONS = 3;
private static final int SEARCH_SIZE = 30;
private UpdatableSearcher searcher;
private Matrix dataPoints;
protected static Matrix multiNormalRandomData(int numDataPoints, int numDimensions) {
Matrix data = new DenseMatrix(numDataPoints, numDimensions);
MultiNormal gen = new MultiNormal(20);
for (MatrixSlice slice : data) {
slice.vector().assign(gen.sample());
}
return data;
}
@Parameterized.Parameters
public static List<Object[]> generateData() {
Matrix dataPoints = multiNormalRandomData(NUM_DATA_POINTS, NUM_DIMENSIONS);
return Arrays.asList(new Object[][]{
{new ProjectionSearch(new EuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE), dataPoints},
{new FastProjectionSearch(new EuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE),
dataPoints},
{new LocalitySensitiveHashSearch(new EuclideanDistanceMeasure(), SEARCH_SIZE), dataPoints},
});
}
public SearchSanityTest(UpdatableSearcher searcher, Matrix dataPoints) {
this.searcher = searcher;
this.dataPoints = dataPoints;
}
@Test
public void testExactMatch() {
searcher.clear();
Iterable<MatrixSlice> data = dataPoints;
final Iterable<MatrixSlice> batch1 = Iterables.limit(data, 300);
List<MatrixSlice> queries = Lists.newArrayList(Iterables.limit(batch1, 100));
// adding the data in multiple batches triggers special code in some searchers
searcher.addAllMatrixSlices(batch1);
assertEquals(300, searcher.size());
Vector q = Iterables.get(data, 0).vector();
List<WeightedThing<Vector>> r = searcher.search(q, 2);
assertEquals(0, r.get(0).getValue().minus(q).norm(1), 1.0e-8);
final Iterable<MatrixSlice> batch2 = Iterables.limit(Iterables.skip(data, 300), 10);
searcher.addAllMatrixSlices(batch2);
assertEquals(310, searcher.size());
q = Iterables.get(data, 302).vector();
r = searcher.search(q, 2);
assertEquals(0, r.get(0).getValue().minus(q).norm(1), 1.0e-8);
searcher.addAllMatrixSlices(Iterables.skip(data, 310));
assertEquals(dataPoints.numRows(), searcher.size());
for (MatrixSlice query : queries) {
r = searcher.search(query.vector(), 2);
assertEquals("Distance has to be about zero", 0, r.get(0).getWeight(), 1.0e-6);
assertEquals("Answer must be substantially the same as query", 0,
r.get(0).getValue().minus(query.vector()).norm(1), 1.0e-8);
assertTrue("Wrong answer must have non-zero distance",
r.get(1).getWeight() > r.get(0).getWeight());
}
}
@Test
public void testNearMatch() {
searcher.clear();
List<MatrixSlice> queries = Lists.newArrayList(Iterables.limit(dataPoints, 100));
searcher.addAllMatrixSlicesAsWeightedVectors(dataPoints);
MultiNormal noise = new MultiNormal(0.01, new DenseVector(20));
for (MatrixSlice slice : queries) {
Vector query = slice.vector();
final Vector epsilon = noise.sample();
List<WeightedThing<Vector>> r = searcher.search(query, 2);
query = query.plus(epsilon);
assertEquals("Distance has to be small", epsilon.norm(2), r.get(0).getWeight(), 1.0e-1);
assertEquals("Answer must be substantially the same as query", epsilon.norm(2),
r.get(0).getValue().minus(query).norm(2), 1.0e-1);
assertTrue("Wrong answer must be further away", r.get(1).getWeight() > r.get(0).getWeight());
}
}
@Test
public void testOrdering() {
searcher.clear();
Matrix queries = new DenseMatrix(100, 20);
MultiNormal gen = new MultiNormal(20);
for (int i = 0; i < 100; i++) {
queries.viewRow(i).assign(gen.sample());
}
searcher.addAllMatrixSlices(dataPoints);
for (MatrixSlice query : queries) {
List<WeightedThing<Vector>> r = searcher.search(query.vector(), 200);
double x = 0;
for (WeightedThing<Vector> thing : r) {
assertTrue("Scores must be monotonic increasing", thing.getWeight() >= x);
x = thing.getWeight();
}
}
}
@Test
public void testRemoval() {
searcher.clear();
searcher.addAllMatrixSlices(dataPoints);
//noinspection ConstantConditions
if (searcher instanceof UpdatableSearcher) {
List<Vector> x = Lists.newArrayList(Iterables.limit(searcher, 2));
int size0 = searcher.size();
List<WeightedThing<Vector>> r0 = searcher.search(x.get(0), 2);
searcher.remove(x.get(0), 1.0e-7);
assertEquals(size0 - 1, searcher.size());
List<WeightedThing<Vector>> r = searcher.search(x.get(0), 1);
assertTrue("Vector should be gone", r.get(0).getWeight() > 0);
assertEquals("Previous second neighbor should be first", 0,
r.get(0).getValue().minus(r0.get(1).getValue()).norm (1), 1.0e-8);
searcher.remove(x.get(1), 1.0e-7);
assertEquals(size0 - 2, searcher.size());
r = searcher.search(x.get(1), 1);
assertTrue("Vector should be gone", r.get(0).getWeight() > 0);
// Vectors don't show up in iterator.
for (Vector v : searcher) {
assertTrue(x.get(0).minus(v).norm(1) > 1.0e-6);
assertTrue(x.get(1).minus(v).norm(1) > 1.0e-6);
}
} else {
try {
List<Vector> x = Lists.newArrayList(Iterables.limit(searcher, 2));
searcher.remove(x.get(0), 1.0e-7);
fail("Shouldn't be able to delete from " + searcher.getClass().getName());
} catch (UnsupportedOperationException e) {
// good enough that UOE is thrown
}
}
}
@Test
public void testSearchFirst() {
searcher.clear();
searcher.addAll(dataPoints);
for (Vector datapoint : dataPoints) {
WeightedThing<Vector> first = searcher.searchFirst(datapoint, false);
WeightedThing<Vector> second = searcher.searchFirst(datapoint, true);
List<WeightedThing<Vector>> firstTwo = searcher.search(datapoint, 2);
assertEquals("First isn't self", 0, first.getWeight(), 0);
assertEquals("First isn't self", datapoint, first.getValue());
assertEquals("First doesn't match", first, firstTwo.get(0));
assertEquals("Second doesn't match", second, firstTwo.get(1));
}
}
@Test
public void testRemove() {
searcher.clear();
for (int i = 0; i < dataPoints.rowSize(); ++i) {
Vector datapoint = dataPoints.viewRow(i);
searcher.add(datapoint);
// As long as points are not searched for right after being added, in FastProjectionSearch, points are not
// merged with the main list right away, so if a search for a point occurs before it's merged the pendingAdditions
// list also needs to be looked at.
// This used to not be the case for searchFirst(), thereby causing removal failures.
if (i % 2 == 0) {
assertTrue("Failed to find self [search]",
searcher.search(datapoint, 1).get(0).getWeight() < Constants.EPSILON);
assertTrue("Failed to find self [searchFirst]",
searcher.searchFirst(datapoint, false).getWeight() < Constants.EPSILON);
assertTrue("Failed to remove self", searcher.remove(datapoint, Constants.EPSILON));
}
}
}
}