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Java example source code file (BasicModelUtils.java)

This example Java source code file (BasicModelUtils.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

arraycomparator, arraylist, basicmodelutils, collection, counter, double, hashset, indarray, inmemorylookuptable, list, map, override, string, util, wordsimilarity

The BasicModelUtils.java Java example source code

package org.deeplearning4j.models.embeddings.reader.impl;

import com.google.common.collect.Lists;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NonNull;
import org.deeplearning4j.berkeley.Counter;
import org.deeplearning4j.models.embeddings.WeightLookupTable;
import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
import org.deeplearning4j.models.embeddings.reader.ModelUtils;
import org.deeplearning4j.models.sequencevectors.sequence.SequenceElement;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.deeplearning4j.util.MathUtils;
import org.deeplearning4j.util.SetUtils;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.ops.transforms.Transforms;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.*;

/**
 * Basic implementation for ModelUtils interface, suited for standalone use.
 *
 * PLEASE NOTE: This reader applies normalization to underlying lookup table.
 *
 * @author Adam Gibson
 */
public class BasicModelUtils<T extends SequenceElement> implements ModelUtils {
    protected volatile VocabCache<T> vocabCache;
    protected volatile WeightLookupTable<T> lookupTable;

    protected volatile boolean normalized  = false;

    private static final Logger log = LoggerFactory.getLogger(BasicModelUtils.class);

    public BasicModelUtils() {

    }

    @Override
    public void init(@NonNull WeightLookupTable<T> lookupTable) {
        this.vocabCache = lookupTable.getVocabCache();
        this.lookupTable = lookupTable;

        // reset normalization trigger on init call
        this.normalized = false;
    }

    /**
     * Returns the similarity of 2 words. Result value will be in range [-1,1], where -1.0 is exact opposite similarity, i.e. NO similarity, and 1.0 is total match of two word vectors.
     * However, most of time you'll see values in range [0,1], but that's something depends of training corpus.
     *
     * Returns NaN if any of labels not exists in vocab, or any label is null
     *
     * @param label1 the first word
     * @param label2 the second word
     * @return a normalized similarity (cosine similarity)
     */
    @Override
    public double similarity( String label1, String label2) {
        if (label1 == null || label2 == null) {
            System.out.println("LABELS: " + label1 + ": " + (label1 == null ? "null": "exists")+ ";" + label2 +" vec2:" + (label2 == null ? "null": "exists"));
            return Double.NaN;
        }

        INDArray vec1 = lookupTable.vector(label1);
        INDArray vec2 = lookupTable.vector(label2);


        if (vec1 == null || vec2 == null) {
            System.out.println(label1 + ": " + (vec1 == null ? "null": "exists")+ ";" + label2 +" vec2:" + (vec2 == null ? "null": "exists"));
            return Double.NaN;
        }

        if (label1.equals(label2)) return 1.0;

        return Transforms.cosineSim(vec1, vec2);
    }


    @Override
    public Collection<String> wordsNearest(String label, int n) {
        Collection<String> collection = wordsNearest(Arrays.asList(label),new ArrayList(),n + 1);
        if (collection.contains(label)) collection.remove(label);
        return collection;
    }

    /**
     * Accuracy based on questions which are a space separated list of strings
     * where the first word is the query word, the next 2 words are negative,
     * and the last word is the predicted word to be nearest
     * @param questions the questions to ask
     * @return the accuracy based on these questions
     */
    @Override
    public Map<String, Double> accuracy(List questions) {
        Map<String,Double> accuracy = new HashMap<>();
        Counter<String> right = new Counter<>();
        String analogyType = "";
        for(String s : questions) {
            if(s.startsWith(":")) {
                double correct = right.getCount("correct");
                double wrong = right.getCount("wrong");
                if(analogyType.isEmpty()){
                    analogyType=s;
                    continue;
                }
                double accuracyRet = 100.0 * correct / (correct + wrong);
                accuracy.put(analogyType,accuracyRet);
                analogyType = s;
                right.clear();
            }
            else {
                String[] split = s.split(" ");
                String word = split[0];
                List<String> positive = Arrays.asList(word);
                List<String> negative = Arrays.asList(split[1],split[2]);
                String predicted = split[3];
                String w = wordsNearest(positive,negative,1).iterator().next();
                if(predicted.equals(w))
                    right.incrementCount("right",1.0);
                else
                    right.incrementCount("wrong",1.0);

            }
        }
        if(!analogyType.isEmpty()){
            double correct = right.getCount("correct");
            double wrong = right.getCount("wrong");
            double accuracyRet = 100.0 * correct / (correct + wrong);
            accuracy.put(analogyType,accuracyRet);
        }
        return accuracy;
    }

    /**
     * Find all words with a similar characters
     * in the vocab
     * @param word the word to compare
     * @param accuracy the accuracy: 0 to 1
     * @return the list of words that are similar in the vocab
     */
    @Override
    public List<String> similarWordsInVocabTo(String word, double accuracy) {
        List<String> ret = new ArrayList<>();
        for(String s : vocabCache.words()) {
            if(MathUtils.stringSimilarity(word, s) >= accuracy)
                ret.add(s);
        }
        return ret;
    }

    public Collection<String> wordsNearest(Collection positive, Collection negative, int top) {
        // Check every word is in the model
        for (String p : SetUtils.union(new HashSet<>(positive), new HashSet<>(negative))) {
            if (!vocabCache.containsWord(p)) {
                return new ArrayList<>();
            }
        }

        INDArray words = Nd4j.create(positive.size() + negative.size(), lookupTable.layerSize());
        int row = 0;
        //Set<String> union = SetUtils.union(new HashSet<>(positive), new HashSet<>(negative));
        for (String s : positive) {
            words.putRow(row++, lookupTable.vector(s));
        }

        for (String s : negative) {
            words.putRow(row++, lookupTable.vector(s).mul(-1));
        }

        INDArray mean = words.isMatrix() ? words.mean(0) : words;

        return wordsNearest(mean, top);
    }

    /**
     * Get the top n words most similar to the given word
     * @param word the word to compare
     * @param n the n to get
     * @return the top n words
     */
    @Override
    public Collection<String> wordsNearestSum(String word, int n) {
        //INDArray vec = Transforms.unitVec(this.lookupTable.vector(word));
        INDArray vec = this.lookupTable.vector(word);
        return wordsNearestSum(vec, n);
    }

    /**
     * Words nearest based on positive and negative words
     * * @param top the top n words
     * @return the words nearest the mean of the words
     */
    @Override
    public Collection<String> wordsNearest(INDArray words, int top) {
        if(lookupTable instanceof InMemoryLookupTable) {
            InMemoryLookupTable l = (InMemoryLookupTable) lookupTable;

            INDArray syn0 = l.getSyn0();

            if (!normalized) {
                synchronized (this) {
                    if (!normalized) {
                        syn0.diviColumnVector(syn0.norm1(1));
                        normalized = true;
                    }
                }
            }

            INDArray similarity = Transforms.unitVec(words).mmul(syn0.transpose());

            List<Double> highToLowSimList = getTopN(similarity, top + 20);

            List<WordSimilarity> result = new ArrayList<>();

            for (int i = 0; i < highToLowSimList.size(); i++) {
                String word = vocabCache.wordAtIndex(highToLowSimList.get(i).intValue());
                if (word != null && !word.equals("UNK") && !word.equals("STOP") ) {
                    INDArray otherVec = lookupTable.vector(word);
                    double sim = Transforms.cosineSim(words, otherVec);

                    result.add(new WordSimilarity(word, sim));
                }
            }

            Collections.sort(result, new SimilarityComparator());

            return getLabels(result, top);
        }

        Counter<String> distances = new Counter<>();

        for(String s : vocabCache.words()) {
            INDArray otherVec = lookupTable.vector(s);
            double sim = Transforms.cosineSim(words, otherVec);
            distances.incrementCount(s, sim);
        }


        distances.keepTopNKeys(top);
        return distances.keySet();


    }

    /**
     * Get top N elements
     *
     * @param vec the vec to extract the top elements from
     * @param N the number of elements to extract
     * @return the indices and the sorted top N elements
     */
    private List<Double> getTopN(INDArray vec, int N) {
        ArrayComparator comparator = new ArrayComparator();
        PriorityQueue<Double[]> queue = new PriorityQueue<>(vec.rows(),comparator);

        for (int j = 0; j < vec.length(); j++) {
            final Double[] pair = new Double[]{vec.getDouble(j), (double) j};
            if (queue.size() < N) {
                queue.add(pair);
            } else {
                Double[] head = queue.peek();
                if (comparator.compare(pair, head) > 0) {
                    queue.poll();
                    queue.add(pair);
                }
            }
        }

        List<Double> lowToHighSimLst = new ArrayList<>();

        while (!queue.isEmpty()) {
            double ind = queue.poll()[1];
            lowToHighSimLst.add(ind);
        }
        return Lists.reverse(lowToHighSimLst);
    }

    /**
     * Words nearest based on positive and negative words
     * * @param top the top n words
     * @return the words nearest the mean of the words
     */
    @Override
    public Collection<String> wordsNearestSum(INDArray words,int top) {

        if(lookupTable instanceof InMemoryLookupTable) {
            InMemoryLookupTable l = (InMemoryLookupTable) lookupTable;
            INDArray syn0 = l.getSyn0();
            INDArray weights = syn0.norm2(0).rdivi(1).muli(words);
            INDArray distances = syn0.mulRowVector(weights).sum(1);
            INDArray[] sorted = Nd4j.sortWithIndices(distances,0,false);
            INDArray sort = sorted[0];
            List<String> ret = new ArrayList<>();
            if(top > sort.length())
                top = sort.length();
            //there will be a redundant word
            int end = top;
            for(int i = 0; i < end; i++) {
                String add = vocabCache.wordAtIndex(sort.getInt(i));
                if(add == null || add.equals("UNK") || add.equals("STOP")) {
                    end++;
                    if(end >= sort.length())
                        break;
                    continue;
                }
                ret.add(vocabCache.wordAtIndex(sort.getInt(i)));
            }
            return ret;
        }

        Counter<String> distances = new Counter<>();

        for(String s : vocabCache.words()) {
            INDArray otherVec = lookupTable.vector(s);
            double sim = Transforms.cosineSim(words, otherVec);
            distances.incrementCount(s, sim);
        }

        distances.keepTopNKeys(top);
        return distances.keySet();
    }

    /**
     * Words nearest based on positive and negative words
     * @param positive the positive words
     * @param negative the negative words
     * @param top the top n words
     * @return the words nearest the mean of the words
     */
    @Override
    public Collection<String> wordsNearestSum(Collection positive, Collection negative, int top) {
        INDArray words = Nd4j.create(lookupTable.layerSize());
    //    Set<String> union = SetUtils.union(new HashSet<>(positive), new HashSet<>(negative));
        for(String s : positive)
            words.addi(lookupTable.vector(s));


        for(String s : negative)
            words.addi(lookupTable.vector(s).mul(-1));

        return wordsNearestSum(words,top);
    }


    private static class SimilarityComparator implements Comparator<WordSimilarity> {
        @Override
        public int compare(WordSimilarity o1, WordSimilarity o2) {
            if (Double.isNaN(o1.getSimilarity()) && Double.isNaN(o2.getSimilarity())) {
                return 0;
            } else if (Double.isNaN(o1.getSimilarity()) && !Double.isNaN(o2.getSimilarity())) {
                return -1;
            } else if (!Double.isNaN(o1.getSimilarity()) && Double.isNaN(o2.getSimilarity())) {
                return 1;
            }
            return Double.compare(o2.getSimilarity(), o1.getSimilarity());
        }
    }

    private static class ArrayComparator implements Comparator<Double[]> {
        @Override
        public int compare(Double[] o1, Double[] o2) {
            if (Double.isNaN(o1[0]) && Double.isNaN(o2[0])) {
                return 0;
            } else if (Double.isNaN(o1[0]) && !Double.isNaN(o2[0])) {
                return -1;
            } else if (!Double.isNaN(o1[0]) && Double.isNaN(o2[0])) {
                return 1;
            }
            return Double.compare(o1[0], o2[0]);
        }
    }

    @Data
    @AllArgsConstructor
    private static class WordSimilarity {
        private String word;
        private double similarity;
    }

    private List<String> getLabels(List results, int limit) {
        List<String> result = new ArrayList<>();
        for (int x = 0; x < results.size(); x++) {
            result.add(results.get(x).getWord());
            if (result.size() >= limit)
                break;
        }

        return result;
    }
}

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