package org.apache.lucene.facet;
import java.util.Random;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field.Store;
import org.apache.lucene.document.StringField;
import org.apache.lucene.facet.FacetsCollector.MatchingDocs;
import org.apache.lucene.facet.taxonomy.FastTaxonomyFacetCounts;
import org.apache.lucene.facet.taxonomy.TaxonomyReader;
import org.apache.lucene.facet.taxonomy.directory.DirectoryTaxonomyReader;
import org.apache.lucene.facet.taxonomy.directory.DirectoryTaxonomyWriter;
import org.apache.lucene.index.RandomIndexWriter;
import org.apache.lucene.index.Term;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.MultiCollector;
import org.apache.lucene.search.TermQuery;
import org.apache.lucene.store.Directory;
import org.apache.lucene.util.IOUtils;
/*
* 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.
*/
public class TestRandomSamplingFacetsCollector extends FacetTestCase {
public void testRandomSampling() throws Exception {
Directory dir = newDirectory();
Directory taxoDir = newDirectory();
DirectoryTaxonomyWriter taxoWriter = new DirectoryTaxonomyWriter(taxoDir);
RandomIndexWriter writer = new RandomIndexWriter(random(), dir);
FacetsConfig config = new FacetsConfig();
int numDocs = atLeast(10000);
for (int i = 0; i < numDocs; i++) {
Document doc = new Document();
doc.add(new StringField("EvenOdd", (i % 2 == 0) ? "even" : "odd", Store.NO));
doc.add(new FacetField("iMod10", String.valueOf(i % 10)));
writer.addDocument(config.build(taxoWriter, doc));
}
Random random = random();
// NRT open
IndexSearcher searcher = newSearcher(writer.getReader());
TaxonomyReader taxoReader = new DirectoryTaxonomyReader(taxoWriter);
IOUtils.close(writer, taxoWriter);
// Test empty results
RandomSamplingFacetsCollector collectRandomZeroResults = new RandomSamplingFacetsCollector(numDocs / 10, random.nextLong());
// There should be no divisions by zero
searcher.search(new TermQuery(new Term("EvenOdd", "NeverMatches")), collectRandomZeroResults);
// There should be no divisions by zero and no null result
assertNotNull(collectRandomZeroResults.getMatchingDocs());
// There should be no results at all
for (MatchingDocs doc : collectRandomZeroResults.getMatchingDocs()) {
assertEquals(0, doc.totalHits);
}
// Now start searching and retrieve results.
// Use a query to select half of the documents.
TermQuery query = new TermQuery(new Term("EvenOdd", "even"));
// there will be 5 facet values (0, 2, 4, 6 and 8), as only the even (i %
// 10) are hits.
// there is a REAL small chance that one of the 5 values will be missed when
// sampling.
// but is that 0.8 (chance not to take a value) ^ 2000 * 5 (any can be
// missing) ~ 10^-193
// so that is probably not going to happen.
int maxNumChildren = 5;
RandomSamplingFacetsCollector random100Percent = new RandomSamplingFacetsCollector(numDocs, random.nextLong()); // no sampling
RandomSamplingFacetsCollector random10Percent = new RandomSamplingFacetsCollector(numDocs / 10, random.nextLong()); // 10 % of total docs, 20% of the hits
FacetsCollector fc = new FacetsCollector();
searcher.search(query, MultiCollector.wrap(fc, random100Percent, random10Percent));
FastTaxonomyFacetCounts random10FacetCounts = new FastTaxonomyFacetCounts(taxoReader, config, random10Percent);
FastTaxonomyFacetCounts random100FacetCounts = new FastTaxonomyFacetCounts(taxoReader, config, random100Percent);
FastTaxonomyFacetCounts exactFacetCounts = new FastTaxonomyFacetCounts(taxoReader, config, fc);
FacetResult random10Result = random10Percent.amortizeFacetCounts(random10FacetCounts.getTopChildren(10, "iMod10"), config, searcher);
FacetResult random100Result = random100FacetCounts.getTopChildren(10, "iMod10");
FacetResult exactResult = exactFacetCounts.getTopChildren(10, "iMod10");
assertEquals(random100Result, exactResult);
// we should have five children, but there is a small chance we have less.
// (see above).
assertTrue(random10Result.childCount <= maxNumChildren);
// there should be one child at least.
assertTrue(random10Result.childCount >= 1);
// now calculate some statistics to determine if the sampled result is 'ok'.
// because random sampling is used, the results will vary each time.
int sum = 0;
for (LabelAndValue lav : random10Result.labelValues) {
sum += lav.value.intValue();
}
float mu = (float) sum / (float) maxNumChildren;
float variance = 0;
for (LabelAndValue lav : random10Result.labelValues) {
variance += Math.pow((mu - lav.value.intValue()), 2);
}
variance = variance / maxNumChildren;
float sigma = (float) Math.sqrt(variance);
// we query only half the documents and have 5 categories. The average
// number of docs in a category will thus be the total divided by 5*2
float targetMu = numDocs / (5.0f * 2.0f);
// the average should be in the range and the standard deviation should not
// be too great
assertTrue(sigma < 200);
assertTrue(targetMu - 3 * sigma < mu && mu < targetMu + 3 * sigma);
IOUtils.close(searcher.getIndexReader(), taxoReader, dir, taxoDir);
}
}