package ivory.bloomir.ranker;
import ivory.bloomir.data.CompressedPostings;
import ivory.bloomir.data.CompressedPostingsIO;
import ivory.bloomir.util.DocumentUtility;
import ivory.bloomir.util.OptionManager;
import ivory.bloomir.util.QueryUtility;
import ivory.core.RetrievalEnvironment;
import ivory.core.data.stat.SpamPercentileScore;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import tl.lin.data.map.HMapIV;
import edu.umd.cloud9.collection.DocnoMapping;
public class SmallAdaptiveRanker {
private CompressedPostings[] postings; //Postings lists
private int[] dfs; //Df values
public SmallAdaptiveRanker(String postingsIndex, FileSystem fs)
throws IOException, ClassNotFoundException {
int numberOfTerms = CompressedPostingsIO.readNumberOfTerms(postingsIndex, fs);
postings = new CompressedPostings[numberOfTerms + 1];
dfs = new int[numberOfTerms + 1];
CompressedPostingsIO.loadPostings(postingsIndex, fs, postings, dfs);
}
/**
* Performs a galloping-search followed by a standard binary search.
*
* @param post Compressed postings list
* @param p An array that holds the decompressed postings for a term.
* This helps remember the decompressed postings and therefore
* avoids redundancy.
* @param low Low index for binary search
* @param high High index for binary search
* @param key Value to search for
* @return An integer array. The elements of this array are as follows:
* [0]: the new 'low' index
* [1]: the value of the key if it was found. This element
* is meaningless otherwise.
*/
private int[] binarySearch(CompressedPostings post, int[] p, int low, int high, int key) {
int lo = low;
int hi = high;
//Gallop
int current = low;
boolean first = true;
while(true) {
if(first) {
first = false;
} else {
if(current == 0) {
current = 1;
} else {
current *= 2;
}
}
if(current > high) {
hi = high;
break;
}
// If the postings from this block are not decompressed
if(p[current] == 0) {
decompress(post, p, post.getBlockNumber(current));
}
if(p[current] < key) {
lo = current;
} else if(p[current] > key) {
hi = current;
break;
} else {
return new int[] {current + 1, p[current]};
}
}
while (lo <= hi) {
int mid = lo + (hi - lo) / 2;
if(p[mid] == 0) {
decompress(post, p, post.getBlockNumber(mid));
}
if (key < p[mid]) {
hi = mid - 1;
} else if (key > p[mid]) {
lo = mid + 1;
} else {
return new int[]{mid + 1, key};
}
}
if(lo <= high) {
return new int[]{lo, p[lo]};
}
return new int[] {lo, p[high]};
}
private final int[] decomp = new int[CompressedPostings.getBlockSize()];
public void decompress(CompressedPostings p, int[] ds, int blockNumber) {
int blSize = p.decompressBlock(decomp, blockNumber);
int pos = p.getBlockStartIndex(blockNumber);
int val = 0;
for(int k = 0; k < blSize; k++) {
val += decomp[k];
ds[pos++] = val;
}
}
public int[] rank(int[] query, int hits) throws IOException {
int[] results = new int[hits];
//If the lenght of the query is one, just return the first n documents
//in the postings list.
if(query.length == 1) {
CompressedPostings ps = postings[query[0]];
int df = dfs[query[0]];
if(hits > df) {
hits = df;
}
int nbBlocks = ps.getBlockCount();
int cnt = 0;
for(int i = 0; i < nbBlocks; i++) {
int bSize = ps.decompressBlock(decomp, i);
int docno = 0;
for(int j = 0; j < bSize; j++) {
docno += decomp[j];
results[cnt] = docno;
cnt++;
if(cnt >= hits) {
return results;
}
}
}
return results;
}
int[] myLow = new int[query.length];
int[] myHigh = new int[query.length];
int[][] ds = new int[query.length][];
for(int i = 0; i < query.length; i++) {
myHigh[i] = dfs[query[i]] - 1;
myLow[i] = 0;
ds[i] = new int[myHigh[i] + 1];
}
decompress(postings[query[0]], ds[0], 0);
int cnt = 0;
int value = ds[0][0];
int index = 1;
int found = 1;
while(true) {
if(index >= query.length) {
index = 0;
}
if(myLow[index] > myHigh[index]) {
return results;
}
int[] r = binarySearch(postings[query[index]], ds[index], myLow[index], myHigh[index], value);
if(r[1] == value) {
found++;
if(found == query.length) {
results[cnt] = value;
cnt++;
if(cnt >= hits) {
return results;
}
found = 1;
int next = r[0];
if(next > myHigh[index]) {
return results;
}
if(ds[index][next] == 0) {
decompress(postings[query[index]], ds[index], postings[query[index]].getBlockNumber(next));
}
value = ds[index][next];
}
} else {
found = 1;
value = r[1];
}
if((r[0] == myLow[index] && r[0] == myHigh[index]) || r[0] > myHigh[index]) {
myLow[index] = myHigh[index] + 1;
} else {
myLow[index] = r[0];
}
index++;
}
}
public static void main(String[] args) throws Exception {
OptionManager options = new OptionManager(SmallAdaptiveRanker.class.getName());
options.addOption(OptionManager.INDEX_ROOT_PATH, "path", "index root", true);
options.addOption(OptionManager.POSTINGS_ROOT_PATH, "path", "Non-positional postings root", true);
options.addOption(OptionManager.QUERY_PATH, "path", "XML query", true);
options.addOption(OptionManager.OUTPUT_PATH, "path", "output path (Optional)", false);
options.addOption(OptionManager.SPAM_PATH, "path", "spam percentile score (Optional)", false);
options.addOption(OptionManager.HITS, "integer", "number of hits (default: 10,000)", false);
options.addDependency(OptionManager.OUTPUT_PATH, OptionManager.SPAM_PATH);
try {
options.parse(args);
} catch(Exception exp) {
return;
}
String indexPath = options.getOptionValue(OptionManager.INDEX_ROOT_PATH);
String postingsIndexPath = options.getOptionValue(OptionManager.POSTINGS_ROOT_PATH);
String queryPath = options.getOptionValue(OptionManager.QUERY_PATH);
boolean writeOutput = options.foundOption(OptionManager.OUTPUT_PATH);
int hits = 10000;
if(options.foundOption(OptionManager.HITS)){
hits = Integer.parseInt(options.getOptionValue(OptionManager.HITS));
}
FileSystem fs = FileSystem.get(new Configuration());
RetrievalEnvironment env = new RetrievalEnvironment(indexPath, fs);
env.initialize(true);
DocnoMapping docnoMapping = env.getDocnoMapping();
SmallAdaptiveRanker ranker = new SmallAdaptiveRanker(postingsIndexPath, fs);
//Parse queries and find integer codes for the query terms.
HMapIV<int[]> queries = QueryUtility.queryToIntegerCode(env, queryPath);
//Evaluate queries and/or write the results to an output file
int[] newDocidsLookup = null;
FSDataOutputStream output = null;
if(writeOutput) {
final SpamPercentileScore spamScores = new SpamPercentileScore();
spamScores.initialize(options.getOptionValue(OptionManager.SPAM_PATH), fs);
newDocidsLookup = DocumentUtility.reverseLookupSpamSortedDocids(DocumentUtility.spamSortDocids(spamScores));
output = fs.create(new Path(options.getOptionValue(OptionManager.OUTPUT_PATH)));
output.write(("<parameters>\n").getBytes());
}
// DocnoMapping docnoMapping = env.getDocnoMapping();
for (int qid: queries.keySet()) {
int[] qterms = queries.get(qid);
if(qterms.length == 0) {
continue;
}
long start = System.nanoTime();
int[] docs = ranker.rank(qterms, hits);
System.out.println(System.nanoTime() - start);
if(writeOutput) {
for(int i = 0; i < docs.length; i++) {
if(docs[i] != 0) {
output.write(("<judgment qid=\"" + qid +
"\" doc=\"" + docnoMapping.getDocid(newDocidsLookup[docs[i]]) +
"\" grade=\"0\" />\n").getBytes());
}
}
}
}
if(writeOutput) {
output.write(("</parameters>\n").getBytes());
output.close();
}
}
}