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/*
* 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.nutch.scoring.similarity.cosine;
import java.lang.invoke.MethodHandles;
import java.util.Collection;
import java.util.Map.Entry;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.util.StringUtils;
import org.apache.nutch.crawl.CrawlDatum;
import org.apache.nutch.metadata.Nutch;
import org.apache.nutch.parse.Parse;
import org.apache.nutch.parse.ParseData;
import org.apache.nutch.protocol.Content;
import org.apache.nutch.scoring.similarity.SimilarityModel;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class CosineSimilarity implements SimilarityModel{
private Configuration conf;
private static final Logger LOG = LoggerFactory
.getLogger(MethodHandles.lookup().lookupClass());
@Override
public void setConf(Configuration conf) {
this.conf = conf;
}
@Override
public float setURLScoreAfterParsing(Text url, Content content, Parse parse) {
float score = 1;
try {
if(!Model.isModelCreated){
Model.createModel(conf);
}
String metatags = parse.getData().getParseMeta().get("metatag.keyword");
String metaDescription = parse.getData().getParseMeta().get("metatag.description");
int[] ngramArr = Model.retrieveNgrams(conf);
int mingram = ngramArr[0];
int maxgram = ngramArr[1];
DocVector docVector = Model.createDocVector(parse.getText()+metaDescription+metatags, mingram, maxgram);
if(docVector!=null){
score = Model.computeCosineSimilarity(docVector);
LOG.info("Setting score of {} to {}",url, score);
}
else {
throw new Exception("Could not create DocVector from parsed text");
}
} catch (Exception e) {
LOG.error("Error creating Cosine Model, setting scores of urls to 1 : {}", StringUtils.stringifyException(e));
}
return score;
}
@Override
public CrawlDatum distributeScoreToOutlinks(Text fromUrl, ParseData parseData,
Collection<Entry<Text, CrawlDatum>> targets, CrawlDatum adjust,
int allCount) {
float score = Float.parseFloat(parseData.getContentMeta().get(Nutch.SCORE_KEY));
for (Entry<Text, CrawlDatum> target : targets) {
target.getValue().setScore(score);
}
return adjust;
}
}