public static void main(String[] args) {
if (args.length == 0) {
System.err.println("Usage: JavaTC <host> [<slices>]");
System.exit(1);
}
JavaSparkContext sc =
new JavaSparkContext(
args[0],
"JavaTC",
System.getenv("SPARK_HOME"),
JavaSparkContext.jarOfClass(JavaTC.class));
Integer slices = (args.length > 1) ? Integer.parseInt(args[1]) : 2;
JavaPairRDD<Integer, Integer> tc = sc.parallelizePairs(generateGraph(), slices).cache();
// Linear transitive closure: each round grows paths by one edge,
// by joining the graph's edges with the already-discovered paths.
// e.g. join the path (y, z) from the TC with the edge (x, y) from
// the graph to obtain the path (x, z).
// Because join() joins on keys, the edges are stored in reversed order.
JavaPairRDD<Integer, Integer> edges =
tc.mapToPair(
new PairFunction<Tuple2<Integer, Integer>, Integer, Integer>() {
@Override
public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> e) {
return new Tuple2<Integer, Integer>(e._2(), e._1());
}
});
long oldCount = 0;
long nextCount = tc.count();
do {
oldCount = nextCount;
// Perform the join, obtaining an RDD of (y, (z, x)) pairs,
// then project the result to obtain the new (x, z) paths.
tc = tc.union(tc.join(edges).mapToPair(ProjectFn.INSTANCE)).distinct().cache();
nextCount = tc.count();
} while (nextCount != oldCount);
System.out.println("TC has " + tc.count() + " edges.");
System.exit(0);
}
/**
* Class used to analyze themes life cycle.
*
* @param hmmInput The hmmInput from which is going to be used the background model, the lexicon
* and the wordStream. Themes must be added before any analytics can be done.
*/
public LifeCycleAnalyserSpark(HmmInputFromParser hmmInput) {
this.wordStream = hmmInput.wordStream;
this.lexicon = hmmInput.lexicon;
this.lexiconAsMap = lexicon.collectAsMap();
getInvertedLexicon();
numberOfThemes = 0L;
numberOfWords = lexicon.count();
// themes = new ArrayList<double[]>();
setBackgroundModelAsThemebyId(hmmInput.backgroundModelById);
}
public static void main(String[] args) {
SparkConf sparkconf =
new SparkConf()
.setAppName("Simple Application")
.setMaster("spark://1.245.77.10:7077")
.set(
"spark.driver.extraClassPath",
"E:/installprogram/spark-1.5.2-bin-hadoop2.4/libthirdparty/*")
.set(
"spark.executor.extraClassPath",
"E:/installprogram/spark-1.5.2-bin-hadoop2.4/libthirdparty/*")
.set("fs.default.name", "file:///");
JavaSparkContext sc = new JavaSparkContext(sparkconf);
Configuration hadoopConfig = sc.hadoopConfiguration();
hadoopConfig.set("fs.hdfs.impl", org.apache.hadoop.hdfs.DistributedFileSystem.class.getName());
hadoopConfig.set("fs.file.impl", org.apache.hadoop.fs.LocalFileSystem.class.getName());
// sc.addJar("e:/installprogram/spark-1.5.2-bin-hadoop2.4/libthirdparty/jmatrw-0.2.jar");
// sc.addJar("e:/installprogram/spark-1.5.2-bin-hadoop2.4/libthirdparty/jmatrw4spark-0.2.jar");
/*JavaRDD<Double> matrdd2 = sc.parallelize(Arrays.asList(1.0, 3.0, 2.0));
System.out.println("Start counting parallelize...");
long values = matrdd2.count();
System.out.println("Value count of parallelize is " + values);*/
JavaPairRDD<Long, Double> matrdd =
sc.newAPIHadoopFile(
"e:/tmp/vecRow03_x256.mat",
JMATFileInputFormat.class,
Long.class,
Double.class,
hadoopConfig);
System.out.println("Start job...");
long values = matrdd.count();
System.out.println("Value count of hadoop is " + values);
sc.stop();
sc.close();
}
/**
* A utility method to generate class classification model summary
*
* @param predictionsAndLabels Predictions and actual labels
* @return Class classification model summary
*/
public static ClassClassificationAndRegressionModelSummary getClassClassificationModelSummary(
JavaSparkContext sparkContext,
JavaRDD<LabeledPoint> testingData,
JavaPairRDD<Double, Double> predictionsAndLabels) {
ClassClassificationAndRegressionModelSummary classClassificationModelSummary =
new ClassClassificationAndRegressionModelSummary();
// store predictions and actuals
List<PredictedVsActual> predictedVsActuals = new ArrayList<PredictedVsActual>();
for (Tuple2<Double, Double> scoreAndLabel : predictionsAndLabels.collect()) {
PredictedVsActual predictedVsActual = new PredictedVsActual();
predictedVsActual.setPredicted(scoreAndLabel._1());
predictedVsActual.setActual(scoreAndLabel._2());
predictedVsActuals.add(predictedVsActual);
}
// create a list of feature values
List<double[]> features = new ArrayList<double[]>();
for (LabeledPoint labeledPoint : testingData.collect()) {
if (labeledPoint != null && labeledPoint.features() != null) {
double[] rowFeatures = labeledPoint.features().toArray();
features.add(rowFeatures);
}
}
// create a list of feature values with predicted vs. actuals
List<TestResultDataPoint> testResultDataPoints = new ArrayList<TestResultDataPoint>();
for (int i = 0; i < features.size(); i++) {
TestResultDataPoint testResultDataPoint = new TestResultDataPoint();
testResultDataPoint.setPredictedVsActual(predictedVsActuals.get(i));
testResultDataPoint.setFeatureValues(features.get(i));
testResultDataPoints.add(testResultDataPoint);
}
// covert List to JavaRDD
JavaRDD<TestResultDataPoint> testResultDataPointsJavaRDD =
sparkContext.parallelize(testResultDataPoints);
// collect RDD as a sampled list
List<TestResultDataPoint> testResultDataPointsSample;
if (testResultDataPointsJavaRDD.count()
> MLCoreServiceValueHolder.getInstance().getSummaryStatSettings().getSampleSize()) {
testResultDataPointsSample =
testResultDataPointsJavaRDD.takeSample(
true,
MLCoreServiceValueHolder.getInstance().getSummaryStatSettings().getSampleSize());
} else {
testResultDataPointsSample = testResultDataPointsJavaRDD.collect();
}
classClassificationModelSummary.setTestResultDataPointsSample(testResultDataPointsSample);
classClassificationModelSummary.setPredictedVsActuals(predictedVsActuals);
// calculate test error
double error =
1.0
* predictionsAndLabels
.filter(
new Function<Tuple2<Double, Double>, Boolean>() {
private static final long serialVersionUID = -3063364114286182333L;
@Override
public Boolean call(Tuple2<Double, Double> pl) {
return !pl._1().equals(pl._2());
}
})
.count()
/ predictionsAndLabels.count();
classClassificationModelSummary.setError(error);
return classClassificationModelSummary;
}
public static void main(String[] args) {
// Path de resultados
String pathResults = "results";
String pathToCategories = "values.txt";
String pathToWords = "words.txt";
File file = new File(pathToWords);
HashMap<Double, String> categoriesDict = new HashMap<>();
HashMap<String, String> resultado = new HashMap<>();
FileInputStream fis = null;
try {
fis = new FileInputStream(pathToCategories);
// Construct BufferedReader from InputStreamReader
BufferedReader br = new BufferedReader(new InputStreamReader(fis));
String line = null;
while ((line = br.readLine()) != null) {
String[] words = line.split(" ");
categoriesDict.put(Double.valueOf(words[0]), words[1]);
}
br.close();
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
}
// Path donde estaran las categorias
String pathCategories = "src/main/resources/categoriestest/";
// Configuracion basica de la aplicacion
SparkConf sparkConf = new SparkConf().setAppName("NaiveBayesTest").setMaster("local[*]");
// Creacion del contexto
JavaSparkContext jsc = new JavaSparkContext(sparkConf);
NaiveBayesModel model = NaiveBayesModel.load(jsc.sc(), pathResults);
HashMap<String, String> dictionary = loadDictionary();
JavaRDD<String> fileWords = null;
if (file.exists()) {
JavaRDD<String> input = jsc.textFile(pathToWords);
fileWords =
input.flatMap(
new FlatMapFunction<String, String>() {
@Override
public Iterable<String> call(String s) throws Exception {
return Arrays.asList(s.split(" "));
}
});
} else {
System.out.println("Error, there is no words");
System.exit(-1);
}
ArrayList<String> aFileWords = (ArrayList<String>) fileWords.collect();
// Cogemos el fichero en el que se encuentran las categorias
File dir = new File(pathCategories);
for (File f : dir.listFiles()) {
JavaRDD<String> input = jsc.textFile(f.getPath());
JavaRDD<String> words =
input.flatMap(
new FlatMapFunction<String, String>() {
@Override
public Iterable<String> call(String s) throws Exception {
return Arrays.asList(s.split(" "));
}
});
JavaPairRDD<String, Double> wordCount = Reducer.parseWords(words, dictionary);
List<Tuple2<String, Double>> total = wordCount.collect();
List<Tuple2<String, Double>> elementsRemoved = new ArrayList<>();
for (Tuple2<String, Double> t : total) {
if (!t._1.equals("")) {
elementsRemoved.add(new Tuple2<>(t._1, t._2 / wordCount.count()));
}
}
ArrayList<Tuple2<String, Double>> freqFinal = new ArrayList<>();
for (String s : aFileWords) {
boolean found = false;
for (Tuple2<String, Double> t : elementsRemoved) {
if (t._1.equals(s)) {
found = true;
freqFinal.add(t);
break;
}
}
if (!found) {
freqFinal.add(new Tuple2<String, Double>(s, 0.0));
}
}
double[] v = new double[freqFinal.size()];
for (int i = 0; i < freqFinal.size(); i++) {
Tuple2<String, Double> t = freqFinal.get(i);
v[i] = t._2;
}
org.apache.spark.mllib.linalg.Vector vector = Vectors.dense(v);
/**/
double d = model.predict(vector);
System.out.println(categoriesDict.get(d));
resultado.put(f.getName(), categoriesDict.get(d));
}
jsc.stop();
try {
Thread.sleep(2000);
} catch (InterruptedException e) {
e.printStackTrace();
}
for (String key : resultado.keySet()) {
System.out.println(key + " - " + resultado.get(key));
}
}
