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Java example source code file (Glove.java)

This example Java source code file (Glove.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

broadcast, double, exception, glove, glovechange, indarray, javardd, list, object, override, pair, string, tuple2, util, vocabword

The Glove.java Java example source code

/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed 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.deeplearning4j.spark.models.embeddings.glove;

import org.apache.commons.math3.util.FastMath;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.broadcast.Broadcast;
import org.deeplearning4j.berkeley.CounterMap;
import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.berkeley.Triple;
import org.deeplearning4j.models.glove.GloveWeightLookupTable;
import org.deeplearning4j.models.word2vec.VocabWord;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.deeplearning4j.spark.models.embeddings.glove.cooccurrences.CoOccurrenceCalculator;
import org.deeplearning4j.spark.models.embeddings.glove.cooccurrences.CoOccurrenceCounts;
import org.deeplearning4j.spark.text.functions.TextPipeline;
import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.AdaGrad;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import scala.Tuple2;

import java.io.Serializable;
import java.util.*;
import java.util.concurrent.atomic.AtomicLong;

import static org.deeplearning4j.spark.models.embeddings.word2vec.Word2VecVariables.*;

/**
 * Spark glove
 *
 * @author Adam Gibson
 */
public class Glove implements Serializable {

    private Broadcast<VocabCache vocabCacheBroadcast;
    private String tokenizerFactoryClazz = DefaultTokenizerFactory.class.getName();
    private boolean symmetric = true;
    private int windowSize = 15;
    private int iterations = 300;
    private static Logger log = LoggerFactory.getLogger(Glove.class);
    /**
     *
     * @param tokenizerFactoryClazz the fully qualified class name of the tokenizer
     * @param symmetric whether the co occurrence counts should be symmetric
     * @param windowSize the window size for co occurrence
     * @param iterations the number of iterations
     */
    public Glove(String tokenizerFactoryClazz, boolean symmetric, int windowSize, int iterations) {
        this.tokenizerFactoryClazz = tokenizerFactoryClazz;
        this.symmetric = symmetric;
        this.windowSize = windowSize;
        this.iterations = iterations;
    }

    /**
     *
     * @param symmetric whether the co occurrence counts should be symmetric
     * @param windowSize the window size for co occurrence
     * @param iterations the number of iterations
     */
    public Glove(boolean symmetric, int windowSize, int iterations) {
        this.symmetric = symmetric;
        this.windowSize = windowSize;
        this.iterations = iterations;
    }


    private Pair<INDArray,Double> update(
            AdaGrad weightAdaGrad
            ,AdaGrad biasAdaGrad
            ,INDArray syn0
            ,INDArray bias
            ,VocabWord w1
            ,INDArray wordVector
            ,INDArray contextVector
            ,double gradient) {
        //gradient for word vectors
        INDArray grad1 =  contextVector.mul(gradient);
        INDArray update = weightAdaGrad.getGradient(grad1,w1.getIndex(),syn0.shape());
        wordVector.subi(update);

        double w1Bias = bias.getDouble(w1.getIndex());
        double biasGradient = biasAdaGrad.getGradient(gradient,w1.getIndex(),bias.shape());
        double update2 = w1Bias - biasGradient;
        bias.putScalar(w1.getIndex(),bias.getDouble(w1.getIndex()) - update2);
        return new Pair<>(update,update2);
    }

    /**
     * Train on the corpus
     * @param rdd the rdd to train
     * @return the vocab and weights
     */
    public Pair<VocabCache train(JavaRDD rdd) throws Exception{
        // Each `train()` can use different parameters
        final JavaSparkContext sc = new JavaSparkContext(rdd.context());
        final SparkConf conf = sc.getConf();
        final int vectorLength = assignVar(VECTOR_LENGTH, conf, Integer.class);
        final boolean useAdaGrad = assignVar(ADAGRAD, conf, Boolean.class);
        final double negative = assignVar(NEGATIVE, conf, Double.class);
        final int numWords = assignVar(NUM_WORDS, conf, Integer.class);
        final int window = assignVar(WINDOW, conf, Integer.class);
        final double alpha = assignVar(ALPHA, conf, Double.class);
        final double minAlpha = assignVar(MIN_ALPHA, conf, Double.class);
        final int iterations = assignVar(ITERATIONS, conf, Integer.class);
        final int nGrams = assignVar(N_GRAMS, conf, Integer.class);
        final String tokenizer = assignVar(TOKENIZER, conf, String.class);
        final String tokenPreprocessor = assignVar(TOKEN_PREPROCESSOR, conf, String.class);
        final boolean removeStop = assignVar(REMOVE_STOPWORDS, conf, Boolean.class);

        Map<String, Object> tokenizerVarMap = new HashMap() {{
            put("numWords", numWords);
            put("nGrams", nGrams);
            put("tokenizer", tokenizer);
            put("tokenPreprocessor", tokenPreprocessor);
            put("removeStop", removeStop);
        }};
        Broadcast<Map broadcastTokenizerVarMap = sc.broadcast(tokenizerVarMap);


        TextPipeline pipeline = new TextPipeline(rdd, broadcastTokenizerVarMap);
        pipeline.buildVocabCache();
        pipeline.buildVocabWordListRDD();


        // Get total word count
        Long totalWordCount = pipeline.getTotalWordCount();
        VocabCache<VocabWord> vocabCache = pipeline.getVocabCache();
        JavaRDD<Pair> sentenceWordsCountRDD = pipeline.getSentenceWordsCountRDD();
        final Pair<VocabCache vocabAndNumWords = new Pair<>(vocabCache, totalWordCount);

        vocabCacheBroadcast = sc.broadcast(vocabAndNumWords.getFirst());

        final GloveWeightLookupTable gloveWeightLookupTable = new GloveWeightLookupTable.Builder()
                .cache(vocabAndNumWords.getFirst()).lr(conf.getDouble(GlovePerformer.ALPHA,0.01))
                .maxCount(conf.getDouble(GlovePerformer.MAX_COUNT,100)).vectorLength(conf.getInt(GlovePerformer.VECTOR_LENGTH,300))
                .xMax(conf.getDouble(GlovePerformer.X_MAX,0.75)).build();
        gloveWeightLookupTable.resetWeights();

        gloveWeightLookupTable.getBiasAdaGrad().historicalGradient = Nd4j.ones(gloveWeightLookupTable.getSyn0().rows());
        gloveWeightLookupTable.getWeightAdaGrad().historicalGradient = Nd4j.ones(gloveWeightLookupTable.getSyn0().shape());


        log.info("Created lookup table of size " + Arrays.toString(gloveWeightLookupTable.getSyn0().shape()));
        CounterMap<String,String> coOccurrenceCounts = sentenceWordsCountRDD.map(new CoOccurrenceCalculator(symmetric,vocabCacheBroadcast, windowSize))
                .fold(new CounterMap<String, String>(), new CoOccurrenceCounts());
        Iterator<Pair pair2 = coOccurrenceCounts.getPairIterator();
        List<Triple counts = new ArrayList<>();

        while(pair2.hasNext()) {
            Pair<String,String> next = pair2.next();
            if(coOccurrenceCounts.getCount(next.getFirst(),next.getSecond()) > gloveWeightLookupTable.getMaxCount()) {
                coOccurrenceCounts.setCount(next.getFirst(),next.getSecond(),gloveWeightLookupTable.getMaxCount());
            }
            counts.add(new Triple<>(next.getFirst(),next.getSecond(),coOccurrenceCounts.getCount(next.getFirst(),next.getSecond())));

        }

        log.info("Calculated co occurrences");

        JavaRDD<Triple parallel = sc.parallelize(counts);
        JavaPairRDD<String, Tuple2 pairs = parallel.mapToPair(new PairFunction, String, Tuple2>() {
            @Override
            public Tuple2<String, Tuple2 call(Triple stringStringDoubleTriple) throws Exception {
                return new Tuple2<>(stringStringDoubleTriple.getFirst(),new Tuple2<>(stringStringDoubleTriple.getSecond(),stringStringDoubleTriple.getThird()));
            }
        });

        JavaPairRDD<VocabWord,Tuple2 pairsVocab = pairs.mapToPair(new PairFunction>, VocabWord, Tuple2>() {
            @Override
            public Tuple2<VocabWord, Tuple2 call(Tuple2> stringTuple2Tuple2) throws Exception {
                VocabWord w1 = vocabCacheBroadcast.getValue().wordFor(stringTuple2Tuple2._1());
                VocabWord w2 = vocabCacheBroadcast.getValue().wordFor(stringTuple2Tuple2._2()._1());
                return new Tuple2<>(w1, new Tuple2<>(w2, stringTuple2Tuple2._2()._2()));
            }
        });


        for(int i = 0; i < iterations; i++) {
            JavaRDD<GloveChange> change = pairsVocab.map(new Function>, GloveChange>() {
                @Override
                public GloveChange call(Tuple2<VocabWord, Tuple2 vocabWordTuple2Tuple2) throws Exception {
                    VocabWord w1 = vocabWordTuple2Tuple2._1();
                    VocabWord w2 = vocabWordTuple2Tuple2._2()._1();
                    INDArray w1Vector = gloveWeightLookupTable.getSyn0().slice(w1.getIndex());
                    INDArray w2Vector = gloveWeightLookupTable.getSyn0().slice(w2.getIndex());
                    INDArray bias = gloveWeightLookupTable.getBias();
                    double score = vocabWordTuple2Tuple2._2()._2();
                    double xMax = gloveWeightLookupTable.getxMax();
                    double maxCount = gloveWeightLookupTable.getMaxCount();
                    //w1 * w2 + bias
                    double prediction = Nd4j.getBlasWrapper().dot(w1Vector, w2Vector);
                    prediction += bias.getDouble(w1.getIndex()) + bias.getDouble(w2.getIndex());

                    double weight = FastMath.pow(Math.min(1.0, (score / maxCount)), xMax);

                    double fDiff = score > xMax ? prediction : weight * (prediction - Math.log(score));
                    if (Double.isNaN(fDiff))
                        fDiff = Nd4j.EPS_THRESHOLD;
                    //amount of change
                    double gradient = fDiff;

                    Pair<INDArray, Double> w1Update = update(
                            gloveWeightLookupTable.getWeightAdaGrad()
                            , gloveWeightLookupTable.getBiasAdaGrad()
                            , gloveWeightLookupTable.getSyn0()
                            , gloveWeightLookupTable.getBias()
                            , w1, w1Vector, w2Vector, gradient);
                    Pair<INDArray, Double> w2Update = update(
                            gloveWeightLookupTable.getWeightAdaGrad()
                            , gloveWeightLookupTable.getBiasAdaGrad()
                            , gloveWeightLookupTable.getSyn0()
                            , gloveWeightLookupTable.getBias()
                            , w2, w2Vector, w1Vector, gradient);
                    return new GloveChange(
                            w1, w2
                            , w1Update.getFirst(), w2Update.getFirst()
                            , w1Update.getSecond(), w2Update.getSecond()
                            , fDiff
                            , gloveWeightLookupTable.getWeightAdaGrad().getHistoricalGradient().slice(w1.getIndex()),
                            gloveWeightLookupTable.getWeightAdaGrad().getHistoricalGradient().slice(w2.getIndex())
                            , gloveWeightLookupTable.getBiasAdaGrad().getHistoricalGradient().getDouble(w2.getIndex())
                            , gloveWeightLookupTable.getBiasAdaGrad().getHistoricalGradient().getDouble(w1.getIndex()));

                }
            });



            List<GloveChange> gloveChanges = change.collect();
            double error = 0.0;
            for(GloveChange change2 : gloveChanges) {
                change2.apply(gloveWeightLookupTable);
                error += change2.getError();
            }


            List l = pairsVocab.collect();
            Collections.shuffle(l);
            pairsVocab = sc.parallelizePairs(l);

            log.info("Error at iteration " + i + " was " + error);



        }

        return new Pair<>(vocabAndNumWords.getFirst(),gloveWeightLookupTable);
    }

}

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