package eu.dnetlib.jobs.featureextraction.lda;

import com.clearspring.analytics.util.Lists;
import eu.dnetlib.featureextraction.Utilities;
import eu.dnetlib.jobs.AbstractSparkJob;
import eu.dnetlib.support.ArgumentApplicationParser;
import eu.dnetlib.support.Author;
import eu.dnetlib.support.AuthorsFactory;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaDoubleRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.SparkSession;
import org.codehaus.jackson.map.ObjectMapper;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import scala.Tuple2;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Optional;
import java.util.stream.Collectors;

public class SparkLDAAnalysis extends AbstractSparkJob {

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

    public SparkLDAAnalysis(ArgumentApplicationParser parser, SparkSession spark) {
        super(parser, spark);
    }
    public static void main(String[] args) throws Exception {
        ArgumentApplicationParser parser = new ArgumentApplicationParser(
                readResource("/jobs/parameters/ldaAnalysis_parameters.json", SparkLDATuning.class)
        );

        parser.parseArgument(args);

        SparkConf conf = new SparkConf();

        new SparkLDAAnalysis(
                parser,
                getSparkSession(conf)
        ).run();
    }

    @Override
    protected void run() throws IOException {
        // read oozie parameters
        final String authorsPath = parser.get("authorsPath");
        final String workingPath = parser.get("workingPath");
        final int numPartitions = Optional
                .ofNullable(parser.get("numPartitions"))
                .map(Integer::valueOf)
                .orElse(NUM_PARTITIONS);

        log.info("authorsPath:           '{}'", authorsPath);
        log.info("workingPath:           '{}'", workingPath);
        log.info("numPartitions:         '{}'", numPartitions);

        JavaSparkContext context = JavaSparkContext.fromSparkContext(spark.sparkContext());

        JavaRDD<Author> authors = context
                .textFile(authorsPath)
                .map(s -> new ObjectMapper().readValue(s, Author.class))
                .filter(a -> !a.getOrcid().isEmpty());  //don't need authors without orcid for the threshold analysis

        JavaRDD<Tuple2<Boolean, Double>> groundTruthThreshold = authors
                .mapToPair(a -> new Tuple2<>(AuthorsFactory.getLNFI(a), a))
                .flatMapToPair(a -> a._1().stream().map(k -> new Tuple2<>(k, a._2())).collect(Collectors.toList()).iterator())
                .groupByKey()
                .flatMap(a -> thresholdAnalysis(a._2()));

        JavaDoubleRDD groundTruthTrue = groundTruthThreshold.filter(Tuple2::_1).mapToDouble(Tuple2::_2);
        long totalPositives = groundTruthTrue.count();
        JavaDoubleRDD groundTruthFalse = groundTruthThreshold.filter(x -> !x._1()).mapToDouble(Tuple2::_2);
        long totalNegatives = groundTruthFalse.count();

        double[] thresholds = new double[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0};

        List<String> stats = new ArrayList<>();
        stats.add("th,fp,fn,tp,tn,total_positives,total_negatives");
        for(double threshold: thresholds) {
            long truePositive = groundTruthTrue.filter(d -> d >= threshold).count();
            long falsePositive = groundTruthFalse.filter(d -> d >= threshold).count();
            long trueNegative = groundTruthFalse.filter(d -> d < threshold).count();
            long falseNegative = groundTruthTrue.filter(d -> d < threshold).count();

            stats.add(threshold + "," + falsePositive + "," + falseNegative + "," + truePositive + "," + trueNegative + "," + totalPositives + "," + totalNegatives);
        }

        Utilities.writeLinesToHDFSFile(stats, workingPath + "/threshold_analysis.csv");

    }

    public Iterator<Tuple2<Boolean, Double>> thresholdAnalysis(Iterable<Author> a) {
        List<Author> authors = Lists.newArrayList(a);

        List<Tuple2<Boolean, Double>> results = new ArrayList<>();
        int i = 0;
        int j = 1;
        while(i < authors.size()) {

            while(j < authors.size()) {
                boolean bRes;
                if(authors.get(i).getOrcid().isEmpty() || authors.get(j).getOrcid().isEmpty())
                    bRes = false;
                else {
                    bRes = authors.get(i).getOrcid().equals(authors.get(j).getOrcid());
                }
                results.add(new Tuple2<>(bRes, cosineSimilarity(authors.get(i).getEmbeddings().get("lda_topics"), authors.get(j).getEmbeddings().get("lda_topics"))));
                j++;
            }
            i++;
            j=i+1;
        }

        return results.iterator();
    }

    double cosineSimilarity(double[] a, double[] b) {
        double dotProduct = 0;
        double normASum = 0;
        double normBSum = 0;

        for(int i = 0; i < a.length; i ++) {
            dotProduct += a[i] * b[i];
            normASum += a[i] * a[i];
            normBSum += b[i] * b[i];
        }

        double eucledianDist = Math.sqrt(normASum) * Math.sqrt(normBSum);
        return dotProduct / eucledianDist;
    }
}