/**
* Cargamos el diccionario
*
* @return
*/
private static HashMap<String, String> loadDictionary() {
HashMap<String, String> map = new HashMap<>();
try (BufferedReader br =
new BufferedReader(new FileReader("src/main/resources/StopWords.txt"))) {
String line;
while ((line = br.readLine()) != null) {
map.put(line, line);
}
} catch (Exception e) {
System.out.println(e.getMessage());
}
return map;
}
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));
}
}
