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Movies Recommender for UrielMtz #19

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130 changes: 130 additions & 0 deletions src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java
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package nearsoft.academy.bigdata.recommendation;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.UserBasedRecommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
//Librerías de mahoot necesarias para el recommender

import java.io.*;
import java.util.*;
import java.util.zip.*;
//Librerías para el manejo de archivos zip en java

public class MovieRecommender {

HashMap<String, Integer> uniqueUser = new HashMap<String, Integer>();
HashMap<String, Integer> uniqueProduct = new HashMap<String, Integer>();
int totalReviews = 0;
//Aquí declaramos las variables globales que usaremos y devolveremos en métodos

public MovieRecommender(String filename) throws IOException{
//Constructor de la clase

InputStream inputStream = new GZIPInputStream(new FileInputStream(filename));
BufferedReader br = new BufferedReader(new InputStreamReader(inputStream, "UTF-8"));
String strCurrentLine;
//Construimos un objeto de BufferedReader para el archivo gzip dado

String prodPattern = "product/productId: ";
String userPattern = "review/userId: ";
String scorePattern = "review/score: ";
//Variables con el patrón que buscaremos en las líneas

String product = "";
int productCont = 0;
String user = "";
int userCont = 0;
String score = "";
//Variables auxiliares donde almacenamos los valores de cada línea

File moviesData = new File("data/moviesdb.csv");
FileWriter writer = new FileWriter(moviesData);
BufferedWriter lineWritter = new BufferedWriter(writer);
//Construimos objeto para la creación y escritura de archivo csv

while((strCurrentLine = br.readLine()) != null){
//Cíclo para recorrer línea a línea del archivo

if(strCurrentLine.contains(prodPattern)) {
product = strCurrentLine.substring(prodPattern.length());
if(!this.uniqueProduct.containsKey(product)){
this.uniqueProduct.put(product, productCont);
productCont++;
}

}
if(strCurrentLine.contains(userPattern)) {
user = strCurrentLine.substring(userPattern.length());
if(!this.uniqueUser.containsKey(user)){
this.uniqueUser.put(user, userCont);
userCont++;
}
}
if(strCurrentLine.contains(scorePattern)) {
this.totalReviews++;
score = strCurrentLine.substring(scorePattern.length());
lineWritter.write(this.uniqueUser.get(user) + "," + this.uniqueProduct.get(product) + "," + score + "\n");
}
//Condicionales para encontrar patrones en las líneas,
//si se encuentra cierto patrón se procesa la línea dependiendo
//el patrón que se encuentr
}
lineWritter.close();
writer.close();
br.close();
inputStream.close();


}

public String getKey(int val)
{
for(String key : this.uniqueProduct.keySet()){
if(this.uniqueProduct.get(key) == val){
return key;
}
}
return null;
}
//Método para devolver el key del hashmap de productos dado un valor

public List<String> getRecommendationsForUser(String user) throws IOException, TasteException {

int numUser = this.uniqueUser.get(user);
List<String> recs = new ArrayList<String>();

DataModel model = new FileDataModel(new File("data/moviesdb.csv"));
UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
List<RecommendedItem> recommendations = recommender.recommend(numUser, 3);
for (RecommendedItem recommendation : recommendations){
recs.add(getKey((int)recommendation.getItemID()));
}
return recs;
}
//Implementación del recommender de 5 min dada la base de datos que constuimos

public int getTotalReviews(){
return this.totalReviews;
}
//Método para devolver el total de reviews

public int getTotalProducts(){
return this.uniqueProduct.size();
}
//Método para devolver el total de productos únicos

public int getTotalUsers(){
return this.uniqueUser.size();
}
//Método para devolver el total de usuarios únicos

}