Saturday, September 17, 2011

Using an Adjacency Map to match Multi-word Phrases

I recently run our entire taxonomy of approximately 1 million medical concepts through my UIMA Aggregate AE for taxonomy mapping described here, and it took 3 weeks. That's right, 3 weeks.

After I was done questioning my programming skills (or lack of it), I began wondering where all the time was being spent. Almost off the bat, I discovered that I had made the newbie mistake of not reusing cursors when reading from the database (its been a while since I've written straight JDBC code), resulting in the code opening and closing each cursor (some up to 20 times) for each of the 1M concepts. Still, that alone could not explain the long run time, so the next candidate was the UIMA AE itself.

Back in my CNET days, over one very late night, I learned to profile applications by inserting stopwatch calls into (my slow) code, and the lesson has stuck (thanks Adam :-)). I wanted to do the same thing here, ie, for a aggregate AE (consisting of a fixed flow of primitive AEs), I wanted to find the time taken within each primitive AE - then I could identify the AEs that needed improvement.

Since the primitive AE is controlled by the UIMA framework, the only way to get a handle to a StopWatch (the only way I know of, anyway) from within each primitive AE is to expose the StopWatch statically via a Singleton holder class, something like this:

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// Source: src/main/java/com/mycompany/tgni/uima/utils/StopwatchHolder.java
package com.mycompany.tgni.uima.utils;

import org.springframework.util.StopWatch;

public class StopwatchHolder {

  private static StopwatchHolder holder = new StopwatchHolder();
  private static StopWatch instance;
  
  private StopwatchHolder() {
    instance = new StopWatch();
  }
  
  public static StopWatch instance() {
    return instance;
  }
  
  public static void reset() {
    if (instance.isRunning()) {
      instance.stop();
    }
    instance = new StopWatch();
  }
}

Once that is done, its a simple matter of calling the start() and stop() methods on the underlying Stopwatch singleton (shown below in my modified code, in case you need to see it). Based on a run of the JUnit test that I used to test the aggregate AE, I discovered that the maximum time in the analysis is spent within the DictionaryAnnotator. Whats more, the DictionaryAnnotator is called 4 times (with different parameters) in a single pass through the aggregate AE, so improving the performance would probably be time well-spent. Here is the output of the StopWatch call before any changes were made.

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    [junit] StopWatch '': running time (millis) = 21
    [junit] -----------------------------------------
    [junit] ms     %     Task name
    [junit] -----------------------------------------
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00013  062%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00001  005%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00001  005%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 
    [junit] ------------- ---------------- ---------------

If you add up the times for the DictionaryAnnotator (except the very first call, which seems to be the framework lazily initializing the AE on the first call to process), the time spent in the DictionaryAnnotator accounts for 1/3 of the total runtime.

If you look at the code (you can find it in my old post here) its easy to see why it could be a problem. The code to shingle the input is essentially an O(n2) operation - the number of shingles produced for an n-word input is the sum of an arithmetic series (k n-word shingles, k-1 n-1 word shingles, and so on) - the sum is computed using the formula for Sn here. Each of these result in a map lookup of O(1).

On the other hand, using an adjacency map to store the collocated words for multi-word phrases in the dictionary, and scanning the input one word at a time, results in n lookups, each of O(1) for an input string of n words, so the complexity of such an algorithm would be O(n). So definitely there seems to be some scope for savings.

Here is the code for the updated DictionaryAnnotator.

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// Source: src/main/java/com/mycompany/tgni/uima/annotators/keyword/DictionaryAnnotator.java
package com.mycompany.tgni.uima.annotators.keyword;

import java.io.IOException;
import java.io.StringReader;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.Stack;

import org.apache.commons.lang.StringUtils;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.core.LowerCaseFilter;
import org.apache.lucene.analysis.core.WhitespaceTokenizer;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.lucene.analysis.tokenattributes.OffsetAttribute;
import org.apache.lucene.util.Version;
import org.apache.uima.UimaContext;
import org.apache.uima.analysis_component.JCasAnnotator_ImplBase;
import org.apache.uima.analysis_engine.AnalysisEngineProcessException;
import org.apache.uima.jcas.JCas;
import org.apache.uima.resource.ResourceAccessException;
import org.apache.uima.resource.ResourceInitializationException;
import org.springframework.util.StopWatch;

import com.mycompany.tgni.uima.conf.SharedMapResource;
import com.mycompany.tgni.uima.conf.SharedSetResource;
import com.mycompany.tgni.uima.utils.StopwatchHolder;

public class DictionaryAnnotator extends JCasAnnotator_ImplBase {

  private String preserveOrTransform;
  private boolean ignoreCase;

  private Map<String,String> dictMap = new HashMap<String,String>();
  private Map<String,Set<String>> collocMap = new HashMap<String,Set<String>>();
  
  private final static String PRESERVE = "preserve";
  private final static String TRANSFORM = "transform";

  private class Word {
    public String word;
    public int start;
    public int end;
  }

  @Override
  public void initialize(UimaContext ctx) 
      throws ResourceInitializationException {
    super.initialize(ctx);
    preserveOrTransform = (String) ctx.getConfigParameterValue(
      "preserveOrTransform");
    ignoreCase = (Boolean) ctx.getConfigParameterValue("ignoreCase");
    try {
      if (PRESERVE.equals(preserveOrTransform)) {
        SharedSetResource res = (SharedSetResource) 
          ctx.getResourceObject("dictAnnotatorProperties");
        for (String dictPhrase : res.getConfig()) {
          dictMap.put(ignoreCase ? 
            StringUtils.lowerCase(dictPhrase) : dictPhrase, null);
        }
      } else if (TRANSFORM.equals(preserveOrTransform)) {
        SharedMapResource res = (SharedMapResource) 
          ctx.getResourceObject("dictAnnotatorProperties");
        Map<String,String> cfg = res.getConfig();
        for (String dictPhrase : cfg.keySet()) {
          dictMap.put(ignoreCase ? 
            StringUtils.lowerCase(dictPhrase) : dictPhrase, 
            cfg.get(dictPhrase));
        }
      } else {
        throw new ResourceInitializationException(
          new IllegalArgumentException(
          "Configuration parameter preserveOrTransform " +
          "must be either 'preserve' or 'transform'"));
      }
      for (String dictPhrase : dictMap.keySet()) {
        String[] words = StringUtils.split(dictPhrase, " ");
        String prevWord = words[0];
        for (int i = 1; i < words.length; i++) {
          if (! collocMap.containsKey(prevWord)) {
            collocMap.put(prevWord, new HashSet<String>());
          }
          collocMap.get(prevWord).add(words[i]);
          prevWord = "_" + words[i];
        }
      }
    } catch (ResourceAccessException e) {
      throw new ResourceInitializationException(e);
    }
  }
  
  @Override
  public void process(JCas jcas) 
      throws AnalysisEngineProcessException {
    StopWatch watch = StopwatchHolder.instance();
    watch.start(getClass().getSimpleName() + "[" + 
      preserveOrTransform + "/" + 
      (ignoreCase ? "ignoreCase" : "matchCase") + "]");
    try {
      Stack<Word> collocations = new Stack<Word>();
      String text = jcas.getDocumentText();
      WhitespaceTokenizer tokenizer = new WhitespaceTokenizer(
        Version.LUCENE_40, new StringReader(text));
      TokenStream tokenStream = ignoreCase ?
        new LowerCaseFilter(Version.LUCENE_40, tokenizer) : tokenizer;
      while (tokenStream.incrementToken()) {
        CharTermAttribute term = 
          (CharTermAttribute) tokenStream.getAttribute(
          CharTermAttribute.class);
        OffsetAttribute offset = 
          (OffsetAttribute) tokenStream.getAttribute(
          OffsetAttribute.class);
        Word word = new Word();
        word.word = term.toString();
        word.start = offset.startOffset();
        word.end = offset.endOffset();
        if (collocations.isEmpty()) {
          // no previous word in stack
          if (collocMap.containsKey(word.word)) {
            collocations.push(word);
          }
        } else {
          // previous word exists, part of phrase
          Word prevWord = collocations.peek();
          Set<String> nextWords = collocMap.get(prevWord.word);
          if (nextWords != null && nextWords.contains(word.word)) {
            word.word = "_" + word.word;
            collocations.push(word);
          } else {
            // complete phrase or single word found, check dictMap
            Word phrase = getPhrase(collocations);
            annotatePhrase(phrase, jcas);
            collocations.clear();
          }
        }
      }
      // end of input, handle trailing stacked words
      if (! collocations.isEmpty()) {
        Word phrase = getPhrase(collocations);
        annotatePhrase(phrase, jcas);
        collocations.clear();
      }
    } catch (IOException e) {
      throw new AnalysisEngineProcessException(e);
    }
    watch.stop();
  }

  private Word getPhrase(Stack<Word> collocations) {
    List<String> words = new ArrayList<String>();
    Word phrase = new Word();
    phrase.start = collocations.elementAt(0).start;
    phrase.end = 0;
    for (Iterator<Word> it = collocations.iterator(); it.hasNext(); ) {
      Word w = it.next();
      words.add(w.word.startsWith("_") ? w.word.substring(1) : w.word);
      phrase.end = w.end;
    }
    phrase.word = StringUtils.join(words.iterator(), " ");
    return phrase;
  }
  
  private void annotatePhrase(Word phrase, JCas jcas) {
    if (dictMap.containsKey(phrase)) {
      KeywordAnnotation annotation = new KeywordAnnotation(jcas);
      annotation.setBegin(phrase.start);
      annotation.setEnd(phrase.end);
      if (TRANSFORM.equals(preserveOrTransform)) {
        annotation.setTransformedValue(dictMap.get(phrase));
      }
      annotation.addToIndexes();
    }
  }
}

The dictionary terms are loaded into two maps in the initialize() method. The first map (dictMap) is a simple map that contains the word or phrase (for multi-word dictionary terms) on the LHS, and the synonym(s) on the RHS if transform is specified. The second one is the adjacency map (collocMap), that looks something like this in JSON format. Note that the value part is really a Set (for fast containment lookup) even though the notation indicates its a List.

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Dictionary Terms
================
vitamin a deficiency
vitamin d deficiency
canine vitamin k deficiency
sun burn

Equivalent Adjacency Map
========================
collocMap = {
  "vitamin" : [ "a", "d" ], 
  "_a" : [ "deficiency" ], 
  "_d" : [ "deficiency" ], 
  "canine" : [ "vitamin" ], 
  "_vitamin" : [ "k" ], 
  "_k" : [ "deficiency" ], 
  "sun" : [ "burn" ]
}

Only words in phrases are stored in the adjacency map. Words other than head words are prefixed with "_" to prevent the code from matching on partial phrases.

Running the JUnit test against the updated code results in the following timings.

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    [junit] StopWatch '': running time (millis) = 22
    [junit] -----------------------------------------
    [junit] ms     %     Task name
    [junit] -----------------------------------------
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00014  064%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00001  005%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00001  005%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00001  005%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00001  005%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 00000  000%  PatternAnnotator[preserve]
    [junit] 00000  000%  PatternAnnotator[transform]
    [junit] 00000  000%  DictionaryAnnotator[preserve/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/matchCase]
    [junit] 00000  000%  DictionaryAnnotator[preserve/ignoreCase]
    [junit] 00000  000%  DictionaryAnnotator[transform/ignoreCase]
    [junit] 
    [junit] ------------- ---------------- ---------------

Not much of a change, as you can see, but now the DictionaryAnnotator takes 1/4 of the total processing time in the test set. Figuring that perhaps my JUnit test strings did not exercise the algorithm enough, I then ran the loader (which calls the aggregate AE, and which took 3 weeks to complete the last time I ran it) over 10K concepts (1/100-th of the full dataset), using the old and new codes, and both times the job finished in about 45 minutes.

My conclusion is that O(n2) performance of the old code approximates the O(n) performance of the new code since n is quite small - most of my synonyms are 2-3 words long, with some outliers. So even though the data doesn't show significant improvement in performance, I will make the change anyway, since the new code has better performance characteristics.

Of course, extrapolating the numbers means that I still need 3.12 days to process the full dataset, which is still kind of high. Since the job lends itself well to parallelization, I am going to try doing that next.

at

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